WO2026060397A2 - Sensor system with improved glucose value estimates - Google Patents

Abstract

An analyte sensor system may include an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host. Sensor electronics may be configured to generate estimated analyte values from the raw sensor signal, determine a rate of change for the estimated analyte values or the raw signal, determine a working electrode temperature, and determine an elapsed time since sensor insertion. The sensor electronics may improve sensor performance by determining a prediction horizon as a function of at least one of the elapsed time and the working electrode temperature, determining a time-lag- compensated estimated analyte value for one of the estimated analyte values as a function of the determined prediction horizon and the rate-of-change, and adjusting estimated analyte values by applying a correction that is a function of the estimated analyte values.

Description

Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 SENSOR SYSTEM WITH IMPROVED GLUCOSE VALUE ESTIMATES CLAIM OF PRIORITY [0001] This application claims the benefit of U.S. Provisional Application No. 63/695,183, filed on September 16, 2024, which is hereby incorporated by reference in its entirety. BACKGROUND [0002] Diabetes is a metabolic condition relating to the production or use of insulin by the body. Insulin is a hormone that allows the body to use glucose for energy or store glucose as fat. [0003] When a person eats a meal that contains carbohydrates, the food is processed by the digestive system, which produces glucose in the person’s blood. Blood glucose can be used for energy or stored as fat. The body normally maintains blood glucose levels in a range that provides sufficient energy to support bodily functions and avoids problems that can arise when glucose levels are too high or too low. Regulation of blood glucose levels depends on the production and use of insulin, which regulates the movement of blood glucose into cells. [0004] When the body does not produce enough insulin or when the body is unable to effectively use the insulin that is present, blood sugar levels can elevate beyond normal ranges. The state of having a higher-than-normal blood sugar level is called “hyperglycemia.” Chronic hyperglycemia can lead to a number of health problems, such as cardiovascular disease, cataract and other eye problems, nerve damage (neuropathy), and kidney damage. Hyperglycemia can also lead to acute problems, such as diabetic ketoacidosis – a state in which the body becomes excessively acidic due to the presence of blood glucose and ketones, which are produced when the body cannot use glucose. The state of having lower-than-normal blood glucose levels is called “hypoglycemia.” Severe hypoglycemia can lead to acute crises that can result in seizures or death. [0005] A diabetes patient can receive insulin to manage blood glucose levels. Insulin can be received, for example, through a manual injection with Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 a needle. Wearable insulin pumps are also available. Diet and exercise also affect blood glucose levels. A glucose sensor can provide an estimated glucose concentration level, which can be used as guidance by a patient or caregiver. [0006] Diabetes conditions are sometimes referred to as “Type 1” and “Type 2.” A Type 1 diabetes patient is typically able to use insulin when it is present, but the body is unable to produce sufficient amounts of insulin, because of a problem with the insulin-producing beta cells of the pancreas. A Type 2 diabetes patient may produce some insulin, but the patient has become “insulin resistant” due to a reduced sensitivity to insulin. The result is that even though insulin is present in the body, the insulin is not sufficiently used by the patient’s body to effectively regulate blood sugar levels. [0007] Blood sugar concentration levels may be monitored with an analyte sensor, such as a continuous glucose monitor. A continuous glucose monitor is used by a host (e.g., patient) to provide information, such as an estimated blood glucose value or a trend of estimated blood glucose levels. SUMMARY [0008] This present application discloses, among other things, systems, devices, and methods related to analyte sensor, including, for example, deploy testing in analyte sensors. [0009] Example 1 is an analyte sensor system including an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host, and sensor electronics configured to perform operations. The operations may include generating estimated analyte values of the analyte concentration based at least on the raw sensor signal, determining a rate-of-change for the estimated analyte values or the raw signal, determining a prediction horizon as a function of at least one of an elapsed time since sensor insertion and a working electrode temperature of a working electrode of the analyte sensor, and generating a time-lag- compensated estimated analyte value for at least one of the estimated analyte values as a function of at least the determined prediction horizon and the Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 rate-of-change, thereby improving performance of the analyte sensor system or adjusting the estimated analyte values by applying a correction that is a function of at least the estimated analyte values, thereby improving performance of the analyte sensor system. [0010] In Example 2, the subject matter of Example 1 optionally includes determining the working electrode temperature and determining the elapsed time since sensor insertion. [0011] In Example 3, the subject matter of Example 2 optionally includes, for a given estimated analyte value, the time-lag-compensated estimated analyte value is determined by summing a time-lag bias term to the given estimated analyte value, and the time-lag bias term is determined as a function of the prediction horizon and the rate-of-change. [0012] In Example 4, the subject matter of Example 3 optionally includes the prediction horizon corresponding to the time for blood glucose to transition from the bloodstream of a host into the interstitial fluid of the host, and the PH is a function of the elapsed time since sensor insertion. [0013] In Example 5, the subject matter of any one or more of Examples 3- 4 includes the prediction horizon that corresponds to the time for blood glucose to transition from the bloodstream of a host into the interstitial fluid of the host, and the prediction horizon is a function of the working electrode temperature. [0014] In Example 6, the subject matter of any one or more of Examples 1- 5 includes the prediction horizon is a function of both the elapsed time since sensor insertion and the working electrode temperature. [0015] In Example 7, the subject matter of any one or more of Examples 1- 6 includes the prediction horizon decreases with increasing elapsed time. [0016] In Example 8, the subject matter of any one or more of Examples 1- 7 includes the prediction horizon decreases with increasing temperature. [0017] In Example 9, the subject matter of any one or more of Examples 1- 8 includes improving sensor performance by at least applying the correction. [0018] In Example 10, the subject matter of Example 9 includes the correction is also a function of the working electrode temperature. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [0019] In Example 11, the subject matter of any one or more of Examples 9-10 includes the correction, which is also a function of the rate of change. [0020] In Example 12, the subject matter of any one or more of Examples 9-11 includes the correction, which is also a function of the elapsed time since sensor insertion. [0021] In Example 13, the subject matter of any one or more of Examples 9-12 includes the correction, which is also a function of the rate-of-change, the working electrode temperature, and the elapsed time since sensor insertion. [0022] In Example 14, the subject matter of any one or more of Examples 1-13 includes a trained computerized model that is used to adjust the estimated analyte values by at least determining the correction. [0023] In Example 15, the subject matter of Example 14 includes inputs to the trained computerized model, including at least one of the estimated analyte values, working electrode temperature, rate-of-change, and elapsed time since sensor insertion. [0024] In Example 16, the subject matter of Example 15 includes the trained computerized model is a neural network. [0025] In Example 17, the subject matter of any one or more of Examples 15-16 includes the sensor electronics are configured to determine adjusted the estimated analyte values by multiplying the estimated analyte value by a first function of the estimated analyte value, the working electrode temperature, rate-of-change, and the elapsed time since sensor insertion and adding a second function of estimated analyte value, the working electrode temperature, the rate-of-change, and the elapsed time since sensor insertion. [0026] In Example 18, the subject matter of Example 17 includes the trained computerized model that outputs the first function and the second function based at least on the inputs to the trained neural network. [0027] In Example 19, the subject matter of Example 18 includes the correction that is applied to improve sensor performance by adding a bias term to the estimated analyte value to minimize the mean absolute relative difference. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [0028] In Example 20, the subject matter of Example 19 includes the bias term is determined by implementing a piecewise curve fitting. [0029] In Example 21, the subject matter of any one or more of Examples 14-20 includes the trained computerized model that applies a capping mechanism to limit adjustments to the estimated analyte values. [0030] In Example 22, the subject matter of Example 21 includes the capping mechanism is applied using a hyperbolic tangent function to limit the adjustments. [0031] In Example 23, the subject matter of Example 22 includes the capping mechanism is applied according to the formula: deltaEGV = nn_correction_cap * tanh(deltaEGV / nn_correction_cap), where deltaEGV represents the difference between neural network corrected values and legacy estimated analyte values. [0032] In Example 24, the subject matter of any one or more of Examples 21-23 includes the capping mechanism is configured to dynamically adjust a correction cap based on historical correction data. [0033] In Example 25, the subject matter of Example 24 includes the capping mechanism is configured to determine the correction cap as a moving average of absolute deltaEGV values over a predetermined time window. [0034] In Example 26, the subject matter of any one or more of Examples 1-25 includes the analyte sensor is a glucose sensor, and the analyte concentration is a glucose concentration. [0035] Example 27 is a method including generating a raw sensor signal, associated with an analyte concentration of a host, using an analyte sensor, and using sensor electronics to perform operations. The operations may include generating estimated analyte values of the analyte concentration based at least on the raw sensor signal, determining a rate-of-change for the estimated analyte values or the raw signal, and determining a prediction horizon as a function of an elapsed time since sensor insertion and a working electrode temperature and generating a time-lag-compensated estimated analyte value for at least one of the estimated analyte values as a function of Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 at least the determined prediction horizon and the rate-of-change, thereby improving performance of the analyte sensor. [0036] In Example 28, the subject matter of Example 27 includes, for a given estimated analyte value the time-lag-compensated estimated analyte value is determined by summing a time-lag bias term to the given estimated analyte value, and the time-lag bias term is determined by multiplying the prediction horizon by the rate-of-change. [0037] In Example 29, the subject matter of Example 28 includes the prediction horizon corresponds to the time for blood glucose to transition from the bloodstream of a host into the interstitial fluid of the host, and the prediction horizon is a function of the elapsed time since sensor insertion. [0038] In Example 30, the subject matter of any one or more of Examples 28-29 includes the prediction horizon that corresponds to time for blood glucose to transition from the bloodstream of a host into the interstitial fluid of the host, and the prediction horizon is a function of the working electrode temperature. [0039] In Example 31, the subject matter of any one or more of Examples 27-30 includes the prediction horizon decreases with increasing elapsed time. [0040] In Example 32, the subject matter of any one or more of Examples 27-31 includes the prediction horizon decreases with increasing temperature. [0041] Example 33 is a method including generating a raw sensor signal, associated with an analyte concentration of a host, using an analyte sensor, and using sensor electronics to perform operations including generating estimated analyte values from the raw sensor signal and adjusting the estimated analyte values by applying a correction that is at least a function of the estimated analyte values, thereby improving performance of the analyte sensor. [0042] In Example 34, the subject matter of Example 33 includes the operations performed using the sensor electronics, including determining a working electrode temperature, and the correction is also a function of the working electrode temperature. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [0043] In Example 35, the subject matter of any one or more of Examples 33-34 includes the operations performed using the sensor electronics, including determining a rate-of-change for the estimated analyte values or the raw signal, and the correction is also a function of the rate-of-change. [0044] In Example 36, the subject matter of any one or more of Examples 33-35 includes the sensor electronics, including determining an elapsed time since sensor insertion, and the correction is also a function of the elapsed time since sensor insertion. [0045] In Example 37, the subject matter of Example 33 includes the operations performed using the sensor electronics, including determining a working electrode temperature, a rate-of-change for the estimated analyte values or the raw signal, and an elapsed time since sensor insertion, and the correction is also a function of the working electrode temperature, the rate- of-change, and the elapsed time since sensor insertion. [0046] In Example 38, the subject matter of any one or more of Examples 33-37 includes the estimated analyte values are adjusted by at least determining the correction using a trained computerized model. [0047] In Example 39, the subject matter of Example 38 includes receiving inputs to the trained computerized model, wherein the inputs include the estimated analyte values, a working electrode temperature, a rate-of-change, and an elapsed time since sensor insertion. [0048] In Example 40, the subject matter of Example 39 includes the trained computerized model is a neural network. [0049] In Example 41, the subject matter of any one or more of Examples 39-40 includes using the sensor electronics to determine adjusted estimated analyte values by multiplying the estimated analyte value by a first function and adding a second function, and both the first function and the second function being functions of the estimated analyte value, the working electrode temperature, the rate-of-change, and the elapsed time since sensor insertion. [0050] In Example 42, the subject matter of Example 41 includes outputting the first function and the second function from the trained Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 computerized model based on at least the inputs to the trained computerized model. [0051] In Example 43, the subject matter of any one or more of Examples 38-42 includes using the trained computerized model to apply a capping mechanism to limit adjustments to the estimated analyte values. [0052] In Example 44, the subject matter of Example 43 includes using the trained computerized model to apply the capping mechanism includes using a hyperbolic tangent function to limit the adjustments. [0053] In Example 45, the subject matter of Example 44 includes the capping mechanism is applied according to the formula: deltaEGV = nn_correction_cap * tanh(deltaEGV / nn_correction_cap), where deltaEGV represents the difference between neural network corrected values and legacy estimated analyte values. [0054] In Example 46, the subject matter of any one or more of Examples 43-45 includes using the capping mechanism to dynamically adjust a correction cap based on historical correction data. [0055] In Example 47, the subject matter of Example 46 includes using the capping mechanism to determine the correction cap as a moving average of absolute deltaEGV values over a predetermined time window. [0056] In Example 48, the subject matter of any one or more of Examples 39-47 includes receiving inputs to the trained computerized model, as well as sensitivity values, background values, and drift and recovery values. [0057] In Example 49, the subject matter of any one or more of Examples 33-48 includes training a computerized model to determine the correction based on inputs that include the estimated analyte value, the working electrode temperature, the rate-of-change for the estimated analyte values or the raw signal, and the elapsed time since sensor insertion. [0058] In Example 50, the subject matter of any one or more of Examples 33-49 includes the applying the correction includes adding a bias term to the estimated analyte value to minimize a mean absolute relative difference. [0059] In Example 51, the subject matter of Example 50 includes implementing piecewise curve fitting to determine the bias term to be added Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 to the estimated analyte value to minimize the mean absolute relative difference. [0060] In Example 52, the subject matter of Example 51 includes the piecewise curve fitting to determine the bias term includes a hyperglycemic curve fitting for the estimated analyte values considered to be too high, a euglycemic curve fitting for the estimated analyte values considered to be in a normally acceptable range, and a hypoglycemic curve fitting for the estimated analyte values considered to be too low. [0061] In Example 53, the subject matter of Example 52 includes the bias term is: for estimated analyte values below 70, the bias term is a first linear function of EGV, for EAVs above 70 and below 180, the bias term is X or the bias term is a constant or a second linear function of EGV, and for EAVs above 180 and below 400, the bias term is an exponential function of EGV. [0062] In Example 54, the subject matter of any one or more of Examples 33-53 includes the analyte sensor is a glucose sensor and the analyte concentration is a glucose concentration. [0063] Example 55 is an analyte sensor system including an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host, and sensor electronics configured to perform operations. The operations may include generating estimated analyte values from the raw sensor signal, determining a rate-of-change for the estimated analyte values, determining a working electrode temperature, determining an elapsed time since sensor insertion, and determining a prediction horizon as a function of at least one of the elapsed time since insertion and the working electrode temperature and determining a time-lag-compensated estimated analyte value for one of the estimated analyte values as a function of the determined prediction horizon and the rate-of-change. [0064] In Example 56, the subject matter of Example 55 includes the analyte sensor system of any one of claims 2 to 26. [0065] Example 57 is an analyte sensor system including an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host, and sensor electronics configured to perform operations. The operations may include generating estimated analyte values Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 from the raw sensor signal, determining a rate-of-change for the estimated analyte values or the raw signal, determining a working electrode temperature, determining an elapsed time since sensor insertion, and adjusting estimated analyte values by applying a correction that is a function of the estimated analyte values. [0066] In Example 58, the subject matter of Example 46 includes the analyte sensor system of any one of claims 2 to 26. [0067] Example 59 is a non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations including generating estimated analyte values of an analyte concentration of a host based at least on a raw sensor signal generated by an analyte sensor, determining a rate-of-change for the estimated analyte values or the raw signal based at least on the raw sensor signal, dynamically determining a prediction horizon as a function of at least one of an elapsed time since sensor insertion and a working electrode temperature of a working electrode of the analyte sensor, and generating a time-lag-compensated estimated analyte value for at least one of the estimated analyte values as a function of at least the determined prediction horizon and the rate-of-change, thereby improving performance of the analyte sensor system or adjusting the estimated analyte values by applying a correction that is a function of at least the estimated analyte values, thereby improving performance of the analyte sensor. [0068] In Example 60, the subject matter of Example 59 includes the operations of any one of claims 1 to 26. [0069] Example 61 is an apparatus including means for generating estimated analyte values of an analyte concentration of a host based at least on a raw sensor signal generated by an analyte sensor, means for determining a rate-of-change for the estimated analyte values or the raw signal based at least on the raw sensor signal, means for dynamically determining a prediction horizon as a function of at least one of an elapsed time since sensor insertion and a working electrode temperature of a working electrode of the analyte sensor, and means for generating a time-lag- compensated estimated analyte value for at least one of the estimated analyte Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 values as a function of at least the determined prediction horizon and the rate-of-change, thereby improving performance of the analyte sensor system or means for adjusting the estimated analyte values by applying a correction that is a function of at least the estimated analyte values, thereby improving performance of the analyte sensor. [0070] In Example 62, the subject matter of Example 61 includes means for performing the operations of any one of claims 1 to 26. [0071] Example 63 is an analyte sensor system including an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host and sensor electronics configured to perform operations including generating estimated analyte values from the raw sensor signal, maintaining historical data over a predetermined time period, applying a trained neural network model that receives as inputs current sensor parameters and the historical data to generate corrected analyte values, and outputting the corrected analyte values for use in analyte monitoring. [0072] In Example 64, the subject matter of Example 63 includes historical data, including a moving average of the estimated analyte values. [0073] In Example 65, the subject matter of Example 64 includes a predetermined time period for the moving average of the estimated analyte values of about 50 minutes. [0074] In Example 66, the subject matter of any one or more of Examples 63-65 includes historical data, including a moving average of the rate of change of the estimated analyte values. [0075] In Example 67, the subject matter of Example 66 includes the predetermined time period for the moving average of the rate of change of the estimated analyte values is about 50 minutes. [0076] In Example 68, the subject matter of any one or more of Examples 63-65 includes the historical data including point-wise historical data for the estimated analyte values. [0077] In Example 69, the subject matter of Example 68 includes the point- wise historical data, which includes equally spaced samples for the estimated analyte values. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [0078] In Example 70, the subject matter of Example 69 includes the equally spaced samples, which include five samples over an immediately preceding five hours. [0079] In Example 71, the subject matter of any one or more of Examples 68-70 includes the point-wise historical data for the estimated analyte values, including samples of a filtered signal divided by a sensitivity value. [0080] In Example 72, the subject matter of any one or more of Examples 63-67 includes the historical data, which includes a moving average of a filtered signal divided by a sensitivity value. [0081] In Example 73, the subject matter of Example 72 includes the predetermined time period for the moving average of the filtered signal /sensitivity is about 50 minutes. [0082] In Example 74, the subject matter of any one or more of Examples 63-73 includes the historical data, which includes a moving average of a measure of noise. [0083] In Example 75, the subject matter of Example 74 includes the predetermined time period for the moving average of the measure of noise of the estimated analyte values is about 50 minutes. [0084] In Example 76, the subject matter of any one or more of Examples 63-75 includes the inputs to the trained neural network, further including an elapsed time since sensor insertion, a rate of change of the estimated analyte values, and a legacy estimated analyte value, the legacy estimated analyte value being determined using another algorithm. [0085] In Example 77, the subject matter of any one or more of Examples 63-76 includes an output of the neural network that provides a correction factor applied to an estimated analyte value algorithm. [0086] In Example 78, the subject matter of Example 77 includes the estimated analyte value algorithm includes inputs indicative of a progressive sensitivity decline, a baseline, and a dip and recovery for the estimated analyte values after implant. [0087] In Example 79, the subject matter of Example 77 includes the estimated analyte value algorithm is based on the correction factor, an estimated sensitivity value, a prediction horizon value, a rate of change Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 value, a filtered signal value, a progressive sensitivity decline value, a dip and recovery value, and a baseline. [0088] In Example 80, the subject matter of any one or more of Examples 63-79 includes implementing a soft capping mechanism using a hyperbolic tangent function to limit the magnitude of corrections applied by the neural network, and outputting final analyte values based on the soft-capped corrections [0089] In Example 81, the subject matter of Example 80 includes implanting the soft capping mechanism, including implementing adaptive capping by calculating a dynamic correction cap based on a moving average of historical correction magnitudes over a time window, applying a soft capping function to limit the correction delta using the dynamic correction cap, and generating final corrected analyte values by applying the soft- capped correction delta to the estimated analyte values. [0090] Example 82 is a method for enhancing analyte sensor accuracy, including generating estimated analyte values from a raw sensor signal, maintaining historical data over a predetermined time period, applying a trained neural network model that receives as inputs current sensor parameters and the historical data to generate corrected analyte values, and outputting the corrected analyte values for use in analyte monitoring. [0091] In Example 83, the subject matter of Example 82 includes the historical data, including a moving average of the estimated analyte values. [0092] In Example 84, the subject matter of Example 83 includes the predetermined time period for the moving average of the estimated analyte values is about 50 minutes. [0093] In Example 85, the subject matter of any one or more of Examples 82-84 includes the historical data, including a moving average of a rate of change of the estimated analyte values. [0094] In Example 86, the subject matter of Example 85 includes the predetermined time period for the moving average of the rate of change of the estimated analyte values is about 50 minutes. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [0095] In Example 87, the subject matter of any one or more of Examples 82-86 includes the historical data includes point-wise historical data for the estimated analyte values. [0096] In Example 88, the subject matter of Example 87 includes the point- wise historical data includes equally spaced samples for the estimated analyte values. [0097] In Example 89, the subject matter of Example 88 includes the equally spaced samples include five samples over an immediately preceding five hours. [0098] In Example 90, the subject matter of any one or more of Examples 87-89 includes the point-wise historical data for the estimated analyte values, which includes samples of a filtered signal divided by a sensitivity value. [0099] In Example 91, the subject matter of any one or more of Examples 82-86 includes the historical data, which includes a moving average of a filtered signal divided by a sensitivity value. [00100] In Example 92, the subject matter of Example 91 includes the predetermined time period for the moving average of the filtered signal /sensitivity is about 50 minutes. [00101] In Example 93, the subject matter of any one or more of Examples 82-92 includes the historical data includes a moving average of a measure of noise. [00102] In Example 94, the subject matter of Example 93 includes the predetermined time period for the moving average of the measure of noise of the estimated analyte values is about 50 minutes. [00103] In Example 95, the subject matter of any one or more of Examples 82-94 includes the inputs to the trained neural network, further including an elapsed time since sensor insertion, a rate of change of the estimated analyte values, and a legacy estimated analyte value, the legacy estimated analyte value being determined using another algorithm. [00104] In Example 96, the subject matter of any one or more of Examples 82-95 includes an output of the neural network that provides a correction factor applied to an estimated analyte value algorithm. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00105] In Example 97, the subject matter of Example 96 includes the estimated analyte value algorithm includes inputs indicative of a progressive sensitivity decline, a baseline, and a dip and recovery for the estimated analyte values after implant. [00106] In Example 98, the subject matter of Example 96 includes the estimated analyte value algorithm is based on the correction factor, an estimated sensitivity value, a prediction horizon value, a rate of change value, a filtered signal value, a progressive sensitivity decline value, a dip and recovery value, and a baseline. [00107] In Example 99, the subject matter of any one or more of Examples 82-98 includes implementing a soft capping mechanism using a hyperbolic tangent function to limit the magnitude of corrections applied by the neural network, and outputting final analyte values based on the soft-capped corrections [00108] In Example 100, the subject matter of Example 99 includes implanting the soft capping mechanism includes implementing adaptive capping by calculating a dynamic correction cap based on a moving average of historical correction magnitudes over a time window, applying a soft capping function to limit the correction delta using the dynamic correction cap, and generating final corrected analyte values by applying the soft- capped correction delta to the estimated analyte values. [00109] In Example 101, the subject matter of any one or more of Examples 82-100 includes the historical data includes moving averages of estimated analyte values, rate-of-change measurements, noise measurements, and residual values over a historical time period, wherein each of the residual values refers to the difference between a filtered and nonfiltered signal determined at an output of a Kalman filter. [00110] Example 102 is an analyte sensor system, including an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host, and sensor electronics configured to perform operations including generating estimated analyte values from the raw sensor signal, applying a neural network correction to the estimated analyte values to generate corrected analyte values, implementing a soft capping Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 mechanism to limit the magnitude of corrections applied by the neural network, and outputting adjusted analyte values based on the soft-capped corrections. [00111] In Example 103, the subject matter of Example 102 includes the soft capping mechanism is implemented by applying a hyperbolic tangent function. [00112] In Example 104, the subject matter of any one or more of Examples 102-103 includes the soft capping mechanism is implemented by implementing adaptive capping. [00113] In Example 105, the subject matter of Example 104 includes implementing adaptive capping, including calculating a dynamic correction cap based on a moving average of historical correction magnitudes over a time window, applying a soft capping function to limit the correction delta using the dynamic correction cap, and generating final corrected analyte values by applying the soft-capped correction delta to the estimated analyte values. [00114] In Example 106, the subject matter of any one or more of Examples 102-105 includes the magnitude of corrections is limited to a maximum of about 25 mg/dl. [00115] In Example 107, the subject matter of any one or more of Examples 102-106 includes the soft capping mechanism limits less than 0.05% of the corrections. [00116] Example 108 is method for enhancing analyte sensor accuracy, including generating a raw sensor signal associated with an analyte concentration of a host, generating estimated analyte values from the raw sensor signal, applying a correction, determined by the neural network, to the estimated analyte values to generate corrected analyte values, implementing a soft capping mechanism to limit the magnitude of corrections applied by the neural network, and outputting adjusted analyte values based on the soft-capped corrections. [00117] In Example 109, the subject matter of Example 108 includes implementing the soft capping mechanism, which includes applying a hyperbolic tangent function. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00118] In Example 110, the subject matter of any one or more of Examples 108-109 includes implanting the soft capping mechanism, which includes implementing adaptive capping. [00119] In Example 111, the subject matter of Example 110 includes implementing adaptive capping, which includes calculating a dynamic correction cap based on a moving average of historical correction magnitudes over a time window, applying a soft capping function to limit the correction delta using the dynamic correction cap, and generating final corrected analyte values by applying the soft-capped correction delta to the estimated analyte values. [00120] In Example 112, the subject matter of any one or more of Examples 108-111 includes the magnitude of corrections is limited to a maximum of about 25 mg/dl. [00121] In Example 113, the subject matter of any one or more of Examples 108-112 includes the soft capping mechanism limits less than 0.05% of the corrections. [00122] Example 114 is a non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations including generating estimated analyte values from a raw sensor signal, maintaining historical data over a predetermined time period, applying a trained neural network model that receives as inputs current sensor parameters and the historical data to generate corrected analyte values, and outputting the corrected analyte values for use in analyte monitoring. [00123] In Example 115, the subject matter of Example 114 includes the operations of any one of Examples 83-101. [00124] Example 116 is an apparatus including means for generating estimated analyte values from a raw sensor signal, means for maintaining historical data over a predetermined time period, means for applying a trained neural network model that receives as inputs current sensor parameters and the historical data to generate corrected analyte values, and means for outputting the corrected analyte values for use in analyte monitoring. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00125] In Example 117, the subject matter of Example 116 includes the operations of any one of Examples 83-101. [00126] Example 118 is a non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations including generating a raw sensor signal associated with an analyte concentration of a host, generating estimated analyte values from the raw sensor signal, applying a correction, determined by the neural network, to the estimated analyte values to generate corrected analyte values, implementing a soft capping mechanism to limit the magnitude of corrections applied by the neural network, and outputting adjusted analyte values based on the soft-capped corrections. [00127] In Example 119, the subject matter of Example 118 includes the operations of any one of Examples 109-113. [00128] Example 120 is an apparatus including means for generating a raw sensor signal associated with an analyte concentration of a host, means for generating estimated analyte values from the raw sensor signal, means for applying a correction, determined by the neural network, to the estimated analyte values to generate corrected analyte values, means for implementing a soft capping mechanism to limit the magnitude of corrections applied by the neural network, and means for outputting adjusted analyte values based on the soft-capped corrections. [00129] In Example 121, the subject matter of Example 120 includes the operations of any one of Examples 109-113. [00130] This summary is intended to provide an overview of the subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the disclosure. The detailed description is included to provide further information about the present patent application. Other aspects of the disclosure will be apparent to persons skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which is not to be taken in a limiting sense. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 BRIEF DESCRIPTION OF THE DRAWINGS [00131] In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. Like numerals having different letter suffixes may represent different instances of similar components. The drawings illustrate generally, by way of example, but not by way of limitation, various embodiments described in the present document. [00132] FIG. 1 is a diagram showing one example of an environment including an analyte sensor system. [00133] FIG. 2 is a schematic illustration of an example analyte sensor system, which may, for example, be the system shown in FIG. 1. [00134] FIG. 3 is a diagram showing one example of a medical device system including the analyte sensor system of FIG. 1. [00135] FIG. 4 is an illustration of an example analyte sensor. [00136] FIG. 5 is an illustration of another example analyte sensor. [00137] FIG. 6 is an enlarged view of an example analyte sensor portion. [00138] FIG. 7 is a cross-sectional view of the analyte sensor of FIGS. 3 and 4. [00139] FIG. 8 is a schematic illustration of a circuit that represents the behavior of an example analyte sensor. [00140] FIG. 9 is a diagram showing one example of a workflow that may be executed by sensor electronics to generate an estimated analyte concentration. [00141] FIG. 10 illustrates, by way of example and not limitation, a dynamically adjusted PH based on eTime. [00142] FIG. 11 illustrates, by way of example and not limitation, a dynamically adjusted correction factor based on temperature. [00143] FIG. 12 illustrates relationships between Mean Absolute Relative Difference (MARD), as a percentage, and working electrode temperature for the fixed prediction horizon (PH) and the adaptive PH, and between MARD, as a percentage, and the time corresponding to a wear period for a sensor. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00144] FIG. 13 illustrates a comparison of EGV (mg/dL) for a fixed PH and for adaptive PH. [00145] FIGS. 14A-14C illustrate examples of functions for adjusting the estimated glucose values (EGVs) based on input(s) to determine an adjusted or final EGV (Equifinal). [00146] FIG. 15 illustrates results after performing a search for a bias term for summing to EGV to minimize MARD. [00147] FIG. 16 illustrates a median relative difference (RD) compensation to generate a corrected EGV using adaptive PH and a progressive sensitivity decline (PSD) for different temperatures. [00148] FIG. 17 illustrates a diagram of a neural network as an example of a model that may be used to determine functions used to determine an adjusted or final EGV based on inputs EGV, working electrode temperature (Twe), rate-of-change (ROC), and elapsed time since insertion (eTime). [00149] FIG. 18 illustrates provides a comparison between performance without relative difference compensation and performance with relative difference compensation. [00150] FIG. 19 illustrates a comparison of correction methods for minimizing a relative difference. [00151] FIG. 20 illustrates a relationship between an EGV multiplication factor (µ) for different inputs [00152] FIG. 21 illustrates a plot of data representing a bias against EGVs and further illustrates a fit for the data. [00153] FIG. 22 illustrates an EGV average offset for different bins of EGV ranges and further illustrates the EGV average offset for a compensation technique that uses PH and PSD and for another compensation technique that uses a neural network with the PH and PSD. [00154] FIG. 23 illustrates a neural network as an example of a model that may be used to determine a correction factor for an algorithm used to determine an estimated analyte value. [00155] FIG. 24A illustrates values for a first algorithm and FIG. 24B illustrates point-wise historical values for ALGORITHM B. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00156] FIG. 25A illustrates a cumulative probability of the deltaEGV, and FIG. 25B illustrates a hyperbolic tangent (tanh) function with 25 mg/dl guardrails. [00157] FIGS. 26A-26B illustrate methods for enhancing analyte sensor accuracy. [00158] FIG. 27 is a block diagram illustrating a computing device hardware architecture, within which a set or sequence of instructions can be executed to cause a machine to perform examples of any one of the methodologies discussed herein. DETAILED DESCRIPTION [00159] Various examples described herein are directed to analyte sensor systems and methods for using analyte sensor systems. An analyte sensor system includes an analyte sensor that is placed in contact with a bodily fluid of a host to measure a concentration of an analyte, such as glucose, in the bodily fluid. In some examples, the analyte sensor is inserted into the host to contact the bodily fluid in vivo. The analyte sensor can be inserted subcutaneously to contact interstitial fluid below the host’s skin. [00160] When the analyte sensor is exposed to analyte in the host’s bodily fluid, electrochemical reactions between the analyte sensor and the analyte cause the analyte sensor to generate a raw sensor signal that is indicative of the analyte concentration in the bodily fluid. For example, the analyte sensor may include two or more electrodes. An analyte sensor system may include sensor electronics to apply a bias condition to the electrodes. The bias condition may be, for example, a potential difference applied between a working electrode of the analyte sensor and a reference electrode of the analyte sensor. The bias condition promotes the electrochemical reaction between the analyte and the analyte sensor, resulting in a current between the working electrode and at least one other analyte sensor electrode. The raw sensor signal may be and/or may be based on the current. [00161] The sensor electronics uses the raw sensor signal to determine an analyte concentration, sometimes referred to as an estimated analyte Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 concentration. In some examples, the sensor electronics is also programmed to output result data, which may include the estimated analyte concentration or other data. In some examples, the sensor electronics communicates result data to one or more other external devices. The sensed analyte may be glucose such that the analyte sensor system is a glucose sensor system which may include a continuous glucose monitoring (CGM) device. The CGM may provide estimated glucose values (EGVs) from the raw sensor signal. Effective diabetes management depends on accurate CGMs. [00162] However, the glucose signal is not directly measured from blood of the host—rather, the glucose signal is measured from interstitial fluid of the host. Further, the measured glucose signal from the interstitial fluid lags behind the actual blood glucose. A time lag compensation model may be used to provide an estimate of the actual blood glucose using the lagging glucose levels measured from the interstitial fluid, but existing algorithms may not sufficiently compensate for this delay. In some systems, PH value (referred to as a fixed prediction horizon coefficient or fixed PH) can be multiplied by the ROC of EGVs or of the raw sensor signal, with or without temperature compensation, to compensate for the time lag for blood glucose to transition from a bloodstream of the host into interstitial fluid of the host. For example, the fixed PH coefficient may be about 7 minutes. The fixed PH may not adequately account for variations in time lag under different conditions such as elapsed time from sensor insertion, rate-of-change of glucose levels, and environmental factors, particularly when there are significant fluctuations in glucose levels or environmental factors affecting sensor performance. Monitoring and accounting for such conditions and environmental factors can be complex, and result in increased computing requirements associated with processing such conditions. Further, processing a large quantity of data associated with monitoring and accounting for such conditions and environmental factors can also be slow and inefficient while requiring significant computing resources. Also, existing CGM models estimate the sensitivity of the sensor using a dual exponential function with inputs of sensor sensitivity values and time, without accounting for the glucose dependency (and other dependencies) on sensitivity. Thus, because existing systems do not account for those conditions and environmental Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 factors, and may be inefficient in processing and monitoring such a large quantity of data associated with accounting for those conditions and environmental factors, estimated analyte concentration values produced using certain existing systems may not achieve the desired level of accuracy and/or performance. [00163] Embodiments of the present subject matter improve sensor performance by more accurately compensating for a time lag between blood glucose and interstitial glucose readings and by improving estimates of sensor sensitivity. This in turn improves the overall accuracy of estimated analyte concentration values generated by the analyte sensor system according to the present subject matter. For example, the present subject matter may improve the PH (e.g., improve the accuracy of the PH) to adjust for time lag based on time after sensor insertion and/or temperature. There is a longer time lag from the actual blood glucose to the lagging glucose levels measured from the interstitial fluid toward the beginning of a wear period. As such, a slightly higher PH may be implemented during an initial wear period (e.g., the first day after insertion, break-in period, etc.), and then the PH may be slightly decreased that for the remainder of the wear session of the days, though other configurations may be contemplated. Also, there is a longer time lag for cooler temperatures. [00164] An adaptive PH model offers a more dynamic and personalized approach to time-lag compensation in CGM devices, potentially leading to improved accuracy and reliability in glucose monitoring for individuals with diabetes. The adaptive PH model according to the present subject matter may dynamically adjust the PH coefficient based on at least one input. The input(s) may include elapsed time from sensor insertion (eTime), rate-of- change of glucose levels, EGV, other sensor-related parameters, and/or environmental conditions (e.g., temperature). Time-lag compensation is improved by defining PH as a function of at least these variables. Improved time-lag compensation enhances the accuracy of glucose predictions in CGM devices. By way of example and not limitation, the adaptive PH model according to the present subject matter may be implemented by defining the PH as an exponential function of eTime and a linear function of the rate-of- change of glucose levels, EGV, and temperature. The parameters of these Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 models may be estimated to achieve improved or optimal performance in terms of relevant metrics such as MARD. MARD reflects a difference from a “gold” standard technique or gold reference for accurately detecting blood glucose values. Thus, the adaptive PH model according to the present subject matter may determine analyte concentration values (e.g., estimated values of analyte concentration levels) with increased accuracy, computing efficiency, and speed. The adaptive PH model according to the present subject matter may additionally, and/or alternatively, reduce the required computing resources associated with monitoring and/or processing a large quantity of data associated with accounting for the sensor-related conditions and/or environmental factors. [00165] Embodiments of the present subject matter improve sensor performance by at least estimating a sensitivity of the sensor as a function of the analyte (e.g., glucose) concentration. An example of a CGM model uses raw current measurement, time, and in vitro measurements to calculate the sensitivity of the sensor. More specifically, example CGM models may use a dual exponential function with the inputs of in vitro measurements (e.g., from the sensor calibration process) and time to estimate the sensor's sensitivity at each time point. [00166] However, it appears that improvements may be made to such models, as additional factors, such as glucose concentration and the rate of its change, can impact the sensitivity of the sensor. Some factors may have more of an impact than others. Calculating sensitivity without these inputs may introduce inaccuracy in EGV calculation. [00167] By way of example and not limitation, more parameters may be incorporated in the sensitivity estimation by deploying a neural network model with input(s) such as EGV, rate of change, time, and/or temperature to estimate a better sensitivity that can lead to improvement in the accuracy of the EGV values. The neural network may act as a general function to be able to capture and find possible function(s) to deploy these inputs to improve sensitivity estimation. The neural network may be placed in different parts of the current CGM model as a correction factor for sensitivity or EGV value. In examples of the described subject matter, a at least a 0.2 reduction in Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 MARD has been achieved. Thus, the CGM model according to the present subject matter may determine analyte concentration values (e.g., estimated values of analyte concentration levels) with increased accuracy, computing efficiency, and speed. The CGM model according to the present subject matter may additionally, and/or alternatively, reduce the required computing resources associated with monitoring and/or processing a large quantity of data associated with accounting for the described inputs. [00168] FIG. 1 is a diagram showing one example of an environment 100 including an analyte sensor system 102. The analyte sensor system 102 is coupled to a host 101, which may be a human patient. In some examples, the host is subject to a temporary or permanent diabetes condition or other health condition that makes analyte monitoring useful. [00169] The analyte sensor system 102 includes an analyte sensor 104. In some examples, the analyte sensor 104 is or includes a glucose sensor configured to measure a glucose concentration in the host 101. The analyte sensor 104 can be exposed to analyte at the host 101 in any suitable way. In some examples, the analyte sensor 104 is fully implantable under the skin of the host 101. In other examples, the analyte sensor 104 is wearable on the body of the host 101 (e.g., on the body but not under the skin). Also, in some examples, the analyte sensor 104 is a transcutaneous device (e.g., with a sensor residing at least partially under or in the skin of a host). It should be understood that the devices and methods described herein can be applied to any device capable of detecting a concentration of an analyte, such as glucose, and providing an output signal that represents the concentration of the analyte. [00170] In the example of FIG. 1, the analyte sensor system 102 also includes sensor electronics 106. In some examples, the sensor electronics 106 and analyte sensor 104 are provided in a single integrated enclosure (See FIG. 4). In other examples, the analyte sensor 104 and sensor electronics 106 are provided as separate components or modules (See FIG. 5). For example, the analyte sensor system 102 may include a disposable (e.g., single-use) sensor mounting unit that may include the analyte sensor 104, a component for attaching the sensor 104 to a host (e.g., an adhesive pad), and/or a Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 mounting structure configured to receive a sensor electronics unit including some or all of the sensor electronics 106 shown in FIGS. 2 and 3. The sensor electronics 106 may be reusable. [00171] The analyte sensor 104 may use any known method, including invasive, minimally-invasive, or non-invasive sensing techniques (e.g., optically excited fluorescence, microneedle, transdermal monitoring of glucose), to provide a raw sensor signal indicative of the concentration of the analyte in the host 101. The raw sensor signal may be converted into calibrated and/or filtered analyte concentration data used to provide a useful value of the analyte concentration (e.g., estimated blood glucose concentration level) to a user, such as the host or a caretaker (e.g., a parent, a relative, a guardian, a teacher, a doctor, a nurse, or any other individual that has an interest in the wellbeing of the host 101). [00172] In some examples, the analyte sensor 104 is or includes a continuous glucose sensor. A continuous glucose sensor can be or include a subcutaneous, transdermal (e.g., transcutaneous), and/or intravascular device. In some embodiments, such a sensor or device may recurrently (e.g., periodically, or intermittently) analyze sensor data. The glucose sensor may use any method of glucose measurement, including enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, immunochemical, and the like. In various examples, the analyte sensor system 102 may be or include a continuous glucose monitor sensor available from DexCom^TM, (e.g., the DexCom G5^TM sensor, DexCom G6^TM sensor, the DexCom G7^TM sensor, or any variation thereof), from Abbott^TM (e.g., the Libre^TM sensor), or from Medtronic^TM (e.g., the Enlite^TM sensor). [00173] In some examples, analyte sensor 104 includes an implantable glucose sensor, such as described with reference to U.S. Patent 6,001,067 and U.S. Patent Publication No. US-2005-0027463-A1, which are incorporated by reference. In some examples, analyte sensor 104 includes a transcutaneous glucose sensor, such as described with reference to U.S. Patent Publication No. US-2006-0020187-A1, which is incorporated by reference. In some examples, analyte sensor 104 may be configured to be Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 implanted in a host vessel or extracorporeally, such as is described in U.S. Patent Publication No. US-2007-0027385-A1, co-pending U.S. Patent Publication No. US-2008-0119703-A1 filed October 4, 2006, U.S. Patent Publication No. US-2008-0108942-A1 filed on March 26, 2007, and U.S. Patent Application No. US-2007-0197890-A1 filed on February 14, 2007, all of which are incorporated by reference. In some examples, the continuous glucose sensor may include a transcutaneous sensor such as described in U.S. Patent 6,565,509 to Say et al., which is incorporated by reference. In some examples, analyte sensor 104 may include a continuous glucose sensor that includes a subcutaneous sensor such as described with reference to U.S. Patent 6,579,690 to Bonnecaze et al. or U.S. Patent 6,484,046 to Say et al., which are incorporated by reference. In some examples, the continuous glucose sensor may include a refillable subcutaneous sensor such as described with reference to U.S. Patent 6,512,939 to Colvin et al., which is incorporated by reference. The continuous glucose sensor may include an intravascular sensor such as described with reference to U.S. Patent 6,477,395 to Schulman et al., which is incorporated by reference. The continuous glucose sensor may include an intravascular sensor such as described with reference to U.S. Patent 6,424,847 to Mastrototaro et al., which is incorporated by reference. Again, while a glucose sensor (e.g., a continuous glucose sensor, etc.) is generally described herein, the analyte sensor may be a lactate sensor, a ketones sensor, an insulin sensor, etc. [00174] The environment 100 may also include various other external devices including, for example, a medical device 108. The medical device 108 may be or include a drug delivery device such as an insulin pump or an insulin pen. In some examples, the medical device 108 includes one or more sensors, such as another analyte sensor, a heart rate sensor, a respiration sensor, a motion sensor (e.g., accelerometer), posture sensor (e.g., 3-axis accelerometer), acoustic sensor (e.g., to capture ambient sound or sounds inside the body). The medical device 108 may be wearable, e.g., on a watch, glasses, contact lens, patch, wristband, ankle band, or another wearable item, or may be incorporated into a handheld device (e.g., a smartphone). In some examples, the medical device 108 includes a multi-sensor patch that may, for example, detect one or more of analyte levels (e.g., glucose, lactate, insulin, Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 ketones, or other substance), heart rate, respiration (e.g., using impedance), activity (e.g., using an accelerometer), posture (e.g., using an accelerometer), galvanic skin response, tissue fluid levels (e.g., using impedance or pressure). [00175] In some examples, the analyte sensor system 102 and the medical device 108 communicate with one another. Communication between the analyte sensor system 102 and medical device 108 may occur over any suitable wired connection and/or via a wireless communication signal 110. For example, the analyte sensor system 102 (e.g., the sensor electronics 106 thereof) may be configured to establish a communication connection with the medical device 108 using a suitable short-range communications medium such as, for example, a radio frequency medium (e.g., Bluetooth, Medical Implant Communication System (MICS), Wi-Fi, near field communication (NFC), radio frequency identification (RFID), Zigbee, Z-Wave or other communication protocols), an optical medium (e.g., infrared), a sonic medium (e.g., ultrasonic), a cellular protocol-based medium (e.g., Code Division Multiple Access (CDMA) or Global System for Mobiles (GSM)), and/or the like. [00176] In some examples, the environment 100 also includes other external devices such as, for example, a wearable sensor 130. The wearable sensor 130 can include a sensor circuit (e.g., a sensor circuit configured to detect a glucose concentration or other analyte concentration) and a communication circuit, which may, for example, be an NFC circuit. In some examples, information from the wearable sensor 130 may be retrieved from the wearable sensor 130 using a user computing device 132, such as a smart phone, that is configured to communicate with the wearable sensor 130 via the wearable sensor’s communication circuit, for example, when the user device 132 is placed near the wearable sensor 130. For example, swiping the user device 132 over the sensor 130 may retrieve sensor data from the wearable sensor 130 using NFC or other suitable wireless communication. [00177] The use of NFC communication may reduce power consumption by the wearable sensor 130, which may reduce the size of a power source (e.g., battery or capacitor) in the wearable sensor 130 or extend the usable life of Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 the power source. In some examples, the wearable sensor 130 may be wearable on an upper arm as shown. In some examples, a wearable sensor 130 may additionally or alternatively be on the upper torso of the patient (e.g., over the heart or over a lung), which may, for example, facilitate detecting heart rate, respiration, or posture. A wearable sensor 136 may also be on the lower body (e.g., on a leg) or other part of the body (e.g., on the abdomen). [00178] In some examples, an array or network of sensors may be associated with the patient. For example, one or more of the analyte sensor system 102, and/or external devices, such as the medical device 108, wearable device 120 such as a watch, an additional wearable sensor 130 and/or the like, may communicate with one another via a short-range communication medium (e.g., Bluetooth, MICS, NFC, or any of the other options described above,). The additional wearable sensor 130 may be any of the examples described above with respect to medical device 108. The analyte sensor system 102, medical device 108, and additional sensor 130 on the host 101 are provided for illustration and description and are not necessarily drawn to scale. [00179] The environment 100 may also include one or more other external devices such as a hand-held smart device (e.g., smart phone) 112, tablet 114, smart pen 116 (e.g., insulin delivery pen with processing and communication capability), computer 118, a wearable device 120 such as a watch, or peripheral medical device 122 (which may be a proprietary device such as a proprietary user device available from DexCom^TM), any of which may communicate with the analyte sensor system 102 via a short-range communication medium, such as indicated by wireless communication signal 110, and may also communicate over a network 124 with a server system (e.g., remote data center) or with a remote terminal 128 to facilitate communication with a remote user (not shown) such as a technical support staff member or a clinician. [00180] The wearable device 120 may include an activity sensor, a heart rate monitor (e.g., light-based sensor or electrode-based sensor), a respiration sensor (e.g., acoustic- or electrode-based), a location sensor (e.g., GPS), or other sensors. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00181] In some examples, the environment 100 includes a server system 126. The server system 126 can include one or more computing devices, such as one or more server computing devices. In some examples, the server system 126 is used to collect analyte data from the analyte sensor system 102 and/or analyte or other data from the plurality of other devices, and to perform analytics on collected data, generate, or apply universal or individualized models for glucose levels, and communicate such analytics, models, or information based thereon back to one or more of the devices in the environment 100. In some examples, the server system 126 gathers inter- host and/or intra-host break-in data to generate one or more break-in characteristics, as described herein. [00182] The environment 100 may also include a wireless access point (WAP) 138 used to communicatively couple one or more of analyte sensor system 102, network 124, server system 126, medical device 108 or any of the peripheral devices described above. For example, WAP 138 may provide Wi-Fi and/or cellular connectivity within environment 100. Other communication protocols, such as NFC or Bluetooth, may also be used among devices of the environment 100. [00183] FIG. 2 is a schematic illustration of an example analyte sensor system 200, which may for example, be the system 102 shown in FIG. 1. The analyte sensor system may include an analyte sensor 202. The analyte sensor 202 may be configured to measure glucose or another suitable analyte. The analyte sensor system 200 may also comprise one or more temperature sensors 204, a processor 210, and a memory 206. The processor 210 may receive a signal indicative of an analyte concentration level from the analyte sensor 202 and receive a temperature signal indicative of a temperature parameter (e.g. absolute or relative temperature, or a temperature gradient) from the temperature sensor 204. The signal indicative of the analyte concentration may be a raw sensor signal or a processed sensor signal. The sensor system 200 may also include one or more additional sensors 208, which may include, for example, a heart rate sensor, activity sensor (e.g. accelerometer), or a pressure gauge (e.g. to measure compression of the sensor against a host). Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00184] The processor 210 may determine a temperature-compensated analyte concentration level based on the temperature sensor signal and optionally also based on one or more signals from additional sensor(s) 208. The processor 210 may determine a specific temperature-compensated sensitivity value (e.g., analyte sensor sensitivity value based on the temperature), or may determine a compensated estimated glucose value. The signal from the temperature sensor 204 may be used as an approximation of a temperature at an analyte sensor, or the signal from the temperature sensor 204 may be processed (e.g., using methods described in detail below) to determine an estimated analyte temperature sensor based on the signal from the temperature sensor 204. [00185] In some examples, the processor 210 may retrieve instructions or information from a memory 206 to determine temperature-compensated analyte concentration level. For example, the processor may access a look-up table, or apply an algorithm based on the signal indicative of analyte concentration and temperature sensor signal or apply the signal indicative of analyte concentration and temperature signal to a model (e.g., use a state model or neural network). [00186] In some examples, the processor may retrieve executable instructions from the memory 206 (or a separate memory that may be operatively coupled to or integrated into the processor.) In some examples, the processor may include, or be part of, an application-specific integrated circuit (ASIC) that may be configured to determine a temperature- compensated glucose concentration level. In various examples, any one or more of the methods described herein may be executed by the processor 210 or temperature-compensated glucose sensor, either alone, or in combination with other processors or devices. [00187] FIG. 3 is a diagram showing one example of a medical device system 300 including the analyte sensor system 102 of FIG. 1. In the example of FIG. 3, the analyte sensor system 102 includes sensor electronics 106 and an example sensor mounting unit 390, although in some examples, it will be appreciated that the analyte sensor 104 and sensor electronics 106 may be included in a common enclosure. While a specific example of Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 division of components between the sensor mounting unit 390 and sensor electronics 106 is shown, it is understood that some examples may include additional components in the sensor mounting unit 390 or in the sensor electronics 106, and that some of the components (e.g., a battery or supercapacitor) that are shown in the sensor electronics 106 may be alternatively or additionally (e.g., redundantly) provided in the sensor mounting unit 390. [00188] In the example shown in FIG. 3, the sensor mounting unit 390 includes the analyte sensor 104 and a battery 392. In some examples, the sensor mounting unit 390 may be replaceable, and the sensor electronics 106 may include a debouncing circuit (e.g., gate with hysteresis or delay) to avoid, for example, recurrent execution of a power-up or power down process when a battery is repeatedly connected and disconnected or avoid processing of noise signal associated with removal or replacement of a battery. [00189] The sensor electronics 106 may include electronics components that are configured to process sensor information, such as raw sensor signals, and generate corresponding analyte concentration values. The sensor electronics 106 may, for example, include electronic circuitry associated with measuring, processing, storing, or communicating continuous analyte sensor data, including prospective algorithms associated with processing and calibration of the raw sensor signal. The sensor electronics 106 may include hardware, firmware, and/or software that enables measurement of levels of the analyte via a glucose sensor. Electronic components may be affixed to a printed circuit board (PCB), or the like, and can take a variety of forms. For example, the electronic components may take the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a microcontroller, and/or a processor. [00190] In the example of FIG. 3, the sensor electronics 106 include a measurement circuit 302 (e.g., potentiostat) coupled to the analyte sensor 104 and configured to recurrently obtain analyte sensor readings using the analyte sensor 104. For example, the measurement circuit 302 may continuously or recurrently sample a raw sensor signal indicating a current Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 flow at the analyte sensor 104 between a working electrode and a reference electrode. The sensor electronics 106 may include a gate circuit 394, which may be used to gate the connection between the measurement circuit 302 and the analyte sensor 104. For example, the analyte sensor 104 may accumulate charge over an accumulation period. After the accumulation period, the gate circuit 394 is opened so that the measurement circuit 302 can sample the accumulated charge. Gating the analyte sensor 104 may improve the performance of the sensor system 102 by creating a larger signal to noise or interference ratio (e.g., because charge accumulates from an analyte reaction, but sources of interference, such as the presence of acetaminophen near a glucose sensor, do not accumulate, or accumulate less than the charge from the analyte reaction). [00191] The sensor electronics 106 may also include a processor 304. The processor 304 is configured to retrieve instructions 306 from memory 308 and execute the instructions 306 to control various operations in the analyte sensor system 102. For example, the processor 304 may be programmed to control application of bias potentials to the analyte sensor 104 via a potentiostat at the measurement circuit 302, interpret raw sensor signals from the analyte sensor 104, and/or compensate for environmental factors. [00192] The processor 304 may also save information in data storage memory 310 or retrieve information from data storage memory 310. In various examples, data storage memory 310 may be integrated with memory 308, or may be a separate memory circuit, such as a non-volatile memory circuit (e.g., flash RAM). Examples of systems and methods for processing sensor analyte data are described in more detail herein and in U.S. Patent Nos. 7,310,544 and 6,931,327. [00193] The sensor electronics 106 may also include one or more sensors, such as the sensor 312, which may be coupled to the processor 304. The sensor 312 may be a temperature sensor, accelerometer, or another suitable sensor. The sensor electronics 106 may also include a power source such as a capacitor or battery 314, which may be integrated into the sensor electronics 106, or may be removable, or part of a separate electronics unit. The battery 314 (or other power storage component, e.g., capacitor) may optionally be Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 rechargeable via a wired or wireless (e.g., inductive or ultrasound) recharging system 316. The recharging system 316 may harvest energy or may receive energy from an external source or on-board source. In various examples, the recharge circuit may include a triboelectric charging circuit, a piezoelectric charging circuit, an RF charging circuit, a light charging circuit, an ultrasonic charging circuit, a heat charging circuit, a heat harvesting circuit, or a circuit that harvests energy from the communication circuit. In some examples, the recharging circuit may recharge the rechargeable battery using power supplied from a replaceable battery (e.g., a battery supplied with a base component). [00194] The sensor electronics 106 may also include one or more supercapacitors in the sensor electronics unit (as shown), or in the sensor mounting unit 390. For example, the supercapacitor may allow energy to be drawn from the battery 314 in a highly consistent manner to extend the life of the battery 314. The battery 314 may recharge the supercapacitor after the supercapacitor delivers energy to the communication circuit or to the processor 304, so that the supercapacitor is prepared for delivery of energy during a subsequent high-load period. In some examples, the supercapacitor may be configured in parallel with the battery 314. A device may be configured to preferentially draw energy from the supercapacitor, as opposed to the battery 314. In some examples, a supercapacitor may be configured to receive energy from a rechargeable battery for short-term storage and transfer energy to the rechargeable battery for long-term storage. [00195] The supercapacitor may extend an operational life of the battery 314 by reducing the strain on the battery 314 during the high-load period. In some examples, a supercapacitor removes at least 10% of the strain off the battery during high-load events. In some examples, a supercapacitor removes at least 30% of the strain off the battery during high-load events. In some examples, a supercapacitor removes at least 30% of the strain off the battery during high-load events. In some examples, a supercapacitor removes at least 50% of the strain off the battery during high-load events. [00196] The sensor electronics 106 may also include a wireless communication circuit 318, which may for example include a wireless Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 transceiver operatively coupled to an antenna. The wireless communication circuit 318 may be operatively coupled to the processor 304 and may be configured to wirelessly communicate with one or more peripheral devices or other medical devices, such as an insulin pump or smart insulin pen. [00197] In the example of FIG. 3, the medical device system 300 also includes optional external devices including, for example, a peripheral device 350. The peripheral device 350 may be any suitable user computing device such as, for example, a wearable device (e.g., activity monitor), such as a wearable device 120. In other examples, the peripheral device 350 may be a hand-held smart device (e.g., smartphone or other device such as a proprietary handheld device available from Dexcom), a tablet 114, a smart pen 116, or special-purpose computer 118 shown in FIG. 1. [00198] The peripheral device 350 may include a UI 352, a memory circuit 354, a processor 356, a wireless communication circuit 358, a sensor 360, or any combination thereof. The peripheral device 350 may not necessarily include all the components shown in FIG. 3. The peripheral device 350 may also include a power source, such as a battery. [00199] The UI 352 may, for example, be provided using any suitable input/output device or devices of the peripheral device 350 such as, for example, a touch-screen interface, a microphone (e.g., to receive voice commands), or a speaker, a vibration circuit, or any combination thereof. The UI 352 may receive information from the host or another user (e.g., instructions, glucose values). The UI 352 may also deliver information to the host or other user, for example, by displaying UI elements at the UI 352. For example, UI elements can indicate glucose or other analyte concentration values, glucose or other analyte trends, glucose, or other analyte alerts, etc. Trends can be indicated by UI elements such as arrows, graphs, charts, etc. [00200] The processor 356 may be configured to present information to a user, or receive input from a user, via the UI 352. The processor 356 may also be configured to store and retrieve information, such as communication information (e.g., pairing information or data center access information), user information, sensor data or trends, or other information in the memory circuit 354. The wireless communication circuit 358 may include a Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 transceiver and antenna configured to communicate via a wireless protocol, such as any of the wireless protocols described herein. The sensor 360 may, for example, include an accelerometer, a temperature sensor, a location sensor, biometric sensor, or blood glucose sensor, blood pressure sensor, heart rate sensor, respiration sensor, or another physiologic sensor. [00201] The peripheral device 350 may be configured to receive and display sensor information that may be transmitted by sensor electronics 106 (e.g., in a customized data package that is transmitted to the display devices based on their respective preferences). Sensor information (e.g., blood glucose concentration level) or an alert or notification (e.g., “high glucose level”, “low glucose level” or “fall rate alert” may be communicated via the UI 352 (e.g., via visual display, sound, or vibration). In some examples, the peripheral device 350 may be configured to display or otherwise communicate the sensor information as it is communicated from the sensor electronics 106 (e.g., in a data package that is transmitted to respective display devices). For example, the peripheral device 350 may transmit data that has been processed (e.g., an estimated analyte concentration level that may be determined by processing raw sensor data), so that a device that receives the data may not be required to further process the data to determine usable information (such as the estimated analyte concentration level). In other examples, the peripheral device 350 may process or interpret the received information (e.g., to declare an alert based on glucose values or a glucose trend). In various examples, the peripheral device 350 may receive information directly from sensor electronics 106, or over a network (e.g., via a cellular or Wi-Fi network that receives information from the sensor electronics 106 or from a device that is communicatively coupled to the sensor electronics 106). [00202] In the example of FIG. 3, the medical device system 300 includes an optional medical device 370. For example, the medical device 370 may be an external device used in addition to or instead of the peripheral device 350. The medical device 370 may be or include any suitable type of medical or other computing device including, for example, the medical device 108, peripheral medical device 122, wearable device 120, wearable sensor 130, or wearable sensor 136 shown in FIG. 1. The medical device 370 may include a Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 UI 372, a memory circuit 374, a processor 376, a wireless communication circuit 378, a sensor 380, a therapy circuit 382, or any combination thereof. [00203] Similar to the UI 352, the UI 372 may be provided using any suitable input/output device or devices of the medical device 370 such as, for example, a touch-screen interface, a microphone, or a speaker, a vibration circuit, or any combination thereof. The UI 372 may receive information from the host or another user (e.g., glucose values, alert preferences, calibration coding). The UI 372 may also deliver information to the host or other user, for example, by displaying UI elements at the UI 352. For example, UI elements can indicate glucose or other analyte concentration values, glucose or other analyte trends, glucose, or other analyte alerts, etc. Trends can be indicated by UI elements such as arrows, graphs, charts, etc. [00204] The processor 376 may be configured to present information to a user, or receive input from a user, via the UI 372. The processor 376 may also be configured to store and retrieve information, such as communication information (e.g., pairing information or data center access information), user information, sensor data or trends, or other information in the memory circuit 374. The wireless communication circuit 378 may include a transceiver and antenna configured communicate via a wireless protocol, such as any of the wireless protocols described herein. [00205] The sensor 380 may, for example, include an accelerometer, a temperature sensor, a location sensor, biometric sensor, or blood glucose sensor, blood pressure sensor, heart rate sensor, respiration sensor, or another physiologic sensor. The medical device 370 may include two or more sensors (or memories or other components), even though only one sensor 380 is shown in the example in FIG. 3. In various examples, the medical device 370 may be a smart handheld glucose sensor (e.g., blood glucose meter), drug pump (e.g., insulin pump), or other physiologic sensor device, therapy device, or combination thereof. [00206] In examples where medical device 370 is or includes an insulin pump, the pump and analyte sensor system 102 may be in two-way communication (e.g., so the pump can request a change to an analyte transmission protocol, e.g., request a data point or request data on a more Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 frequent schedule), or the pump and analyte sensor system 102 may communicate using one-way communication (e.g., the pump may receive analyte concentration level information from the analyte sensor system). In one-way communication, a glucose value may be incorporated in an advertisement message, which may be encrypted with a previously shared key. In a two-way communication, a pump may request a value, which the analyte sensor system 102 may share, or obtain and share, in response to the request from the pump, and any or all of these communications may be encrypted using one or more previously shared keys. An insulin pump may receive and track analyte (e.g., glucose) values transmitted from analyte sensor system 102 using one-way communication to the pump for one or more of a variety of reasons. For example, an insulin pump may suspend or activate insulin administration based on a glucose value being below or above a threshold value. [00207] In some examples, the medical device system 300 includes two or more peripheral devices and/or medical devices that each receive information directly or indirectly from the analyte sensor system 102. Because different display devices provide many different user interfaces, the content of the data packages (e.g., amount, format, and/or type of data to be displayed, alarms, and the like) may be customized (e.g., programmed differently by the manufacturer and/or by an end user) for each device. For example, referring now to the example of FIG. 1, a plurality of different peripheral devices may be in direct wireless communication with sensor electronics 106 (e.g., such as an sensor electronics 106 that are on-skin and physically connected to the continuous analyte sensor 104) during a sensor session to enable a plurality of different types and/or levels of display and/or functionality associated with the displayable sensor information, or, to save battery power in the sensor system 102, one or more specified devices may communicate with the analyte sensor system 102 and relay (i.e., share) information to other devices directly or through a server system (e.g., a network-connected data center) 126. [00208] FIG. 4 is a side view of an example analyte sensor 434 that may be implanted into a host. An enclosure 402 may be adhered to the host's skin using an adhesive pad 408. The adhesive pad 408 may be formed from an Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 extensible material, which may be removably attached to the skin using an adhesive. Sensor electronics may be positioned within the enclosure 402. The sensor 434 may extend from the enclosure 402 and under the skin of a host, as shown. [00209] FIG. 5 is a side view of another example analyte sensor 534 in an arrangement including a mounting unit 514 and an electronics unit 518. The mounting unit 514 may be adhered to the host's skin using an adhesive pad 508, which may be like the adhesive pad 408 described herein. The electronics unit 518 comprises an enclosure 502 that may have sensor electronics positioned thereon. In some examples, the electronics unit 518 and mounting unit 514 are arranged in a manner like the sensor electronics 106 and sensor mounting unit 390 shown in FIGS. 1 and 4. For example, the sensor 534 may extend from the enclosure 502 via the mounting unit 514. [00210] FIG. 6 is an enlarged view of a distal portion of an analyte sensor 634. The analyte sensor 634 illustrates one example arrangement that may be used to implement the analyte sensors described herein, such as, for example, the analyte sensors 104, 434, 534. The analyte sensor 634 may be adapted for insertion under the host's skin and may be mechanically coupled to an enclosure, such as the enclosures 502, and/or to a mounting unit 514, such as the mounting unit 514. The analyte sensor 634 may be electrically coupled to sensor electronics, which may be positioned within the enclosure 402, 502. [00211] The example analyte sensor 634 shown in FIG. 6 includes an elongated conductive body 641. The elongated conductive body 641 can include a core with various layers positioned thereon. A first layer 638 that at least partially surrounds the core and includes a working electrode, for example located in window 639). In some examples, the core and the first layer 638 are made of a single material (such as, for example, platinum). In some examples, the elongated conductive body 641 is a composite of two conductive materials, or a composite of at least one conductive material and at least one non-conductive material. A membrane system 632 is located over the working electrode and may cover other layers and/or electrodes of the sensor 634, as described herein. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00212] The first layer 638 may be formed of a conductive material. The working electrode (at window 639) is an exposed portion of the surface of the first layer 638. Accordingly, the first layer 638 is formed of a material configured to provide a suitable electroactive surface for the working electrode. Examples of suitable materials include, but are not limited to, platinum, platinum-iridium, gold, palladium, iridium, graphite, carbon, a conductive polymer, an alloy, and/or the like. [00213] A second layer 640 surrounds at least a portion of the first layer 638, thereby defining boundaries of the working electrode. In some examples, the second layer 640 serves as an insulator and is formed of an insulating material, such as polyimide, polyurethane, parylene, or any other suitable insulating materials or materials. In some examples, the second layer 640 is configured such that the working electrode (of the layer 638) is exposed via the window 639. [00214] In some examples, the sensor 634 further includes a third layer 643 comprising a conductive material. The third layer 643 may comprise a reference electrode. In some examples, the third layer 643, including the reference electrode, is formed of a silver-containing material that is applied onto the second layer 640 (e.g., an insulator). The silver-containing material may include various materials and be in various forms such as, for example, Ag/AgCl-polymer pasts, paints, polymer-based conducting mixtures, inks, etc. [00215] The analyte sensor 634 may include two (or more) electrodes, e.g., a working electrode at the layer 638 and exposed at window 639 and at least one additional electrode, such as a reference electrode of the layer 643. In the example arrangement of FIGS. 6-7, the reference electrode also functions as a counter electrode, although other arrangements can include a separate counter electrode. While the analyte sensor 634 may be used with a mounting unit in some examples, in other examples, the analyte sensor 634 may be used with other types of sensor systems. For example, the analyte sensor 634 may be part of a system that includes a battery and sensor in a single package, and may optionally include, for example, a near-field communication (NFC) circuit. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00216] FIG. 7 is a cross-sectional view through the sensor 634 of FIG. 6 on plane 2-2 illustrating a membrane system 632. The membrane system 632 may include a number of domains (e.g., layers). In an example, the membrane system 632 may include an enzyme domain 642, a diffusion resistance domain 644, and a bioprotective domain 646 located around the working electrode. In some examples, a unitary diffusion resistance domain and bioprotective domain may be included in the membrane system 632 (e.g., wherein the functionality of both the diffusion resistance domain and bioprotective domain are incorporated into one domain). [00217] The membrane system 632, in some examples, also includes an electrode layer 647. The electrode layer 647 may be arranged to provide an environment between the surfaces of the working electrode and the reference electrode that facilitates the electrochemical reaction between the electrodes. For example, the electrode layer 647 may include a coating that maintains a layer of water at the electrochemically reactive surfaces of the sensor 634. [00218] In some examples, the sensor 634 may be configured for short-term implantation (e.g., from about 1 to 30 days). However, it is understood that the membrane system 632 can be modified for use in other devices, for example, by including only one or more of the domains, or additional domains. For example, a membrane system 632 may include a plurality of resistance layers, or a plurality of enzyme layers. In some examples, the resistance domain 644 may include a plurality of resistance layers, or the enzyme domain 642 may include a plurality of enzyme layers. [00219] The diffusion resistance domain 644 may include a semipermeable membrane that controls the flux of oxygen and glucose to the underlying enzyme domain 642. As a result, the upper limit of linearity of glucose measurement is extended to a much higher value than that which is achieved without the diffusion resistance domain 644. [00220] In some examples, the membrane system 632 may include a bioprotective domain 646, also referred to as a domain or biointerface domain, comprising a base polymer. However, the membrane system 632 of some examples can also include a plurality of domains or layers including, for example, an electrode domain, an interference domain, or a cell Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 disruptive domain, such as described in more detail elsewhere herein and in U.S. Patent Nos. 7,494,465, 8,682,608, and 9,044,199, which are incorporated herein by reference in their entirety. [00221] It is to be understood that sensing membranes modified for other sensors, for example, may include fewer or additional layers. For example, in some examples, the membrane system 632 may comprise one electrode layer, one enzyme layer, and two bioprotective layers, but in other examples, the membrane system 632 may comprise one electrode layer, two enzyme layers, and one bioprotective layer. In some examples, the bioprotective layer may be configured to function as the diffusion resistance domain 644 and control the flux of the analyte (e.g., glucose) to the underlying membrane layers. [00222] In some examples, one or more domains of the sensing membranes may be formed from materials such as silicone, polytetrafluoroethylene, polyethylene-co-tetrafluoroethylene, polyolefin, polyester, polycarbonate, biostable polytetrafluoroethylene, homopolymers, copolymers, terpolymers of polyurethanes, polypropylene (PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF), polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA), polyether ether ketone (PEEK), polyurethanes, cellulosic polymers, poly(ethylene oxide), poly(propylene oxide) and copolymers and blends thereof, polysulfones and block copolymers thereof including, for example, di-block, tri-block, alternating, random and graft copolymers. [00223] In some examples, the sensing membrane can be deposited on the electroactive surfaces of the electrode material using known thin or thick film techniques (for example, spraying, electro-depositing, dipping, or the like). The sensing membrane located over the working electrode does not have to have the same structure as the sensing membrane located over the reference electrode; for example, the enzyme domain 642 deposited over the working electrode does not necessarily need to be deposited over the reference or counter electrodes. [00224] Although the examples illustrated in FIGS. 6-7 involve circumferentially extending membrane systems, the membranes described Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 herein may be applied to any planar or non-planar surface, for example, the substrate-based sensor structure of U.S. Pat. No. 6,565,509 to Say et al., which is incorporated by reference. [00225] In an example in which the analyte sensor 634 is a glucose sensor, glucose analyte can be detected utilizing glucose oxidase. Glucose oxidase reacts with glucose to produce hydrogen peroxide (H₂O₂). The hydrogen peroxide reacts with the surface of the working electrode, producing two protons (2H⁺), two electrons (2e⁻) and one molecule of oxygen (O₂). This produces an electronic current that may be detected by the sensor electronics 106. The amount of current is a function of the glucose concentration level. A calibration curve may be used to provide an estimated glucose concentration level based on a measured current. The amount of current is also a function of the diffusivity of glucose through the sensor membrane. The glucose diffusivity may change over time, which may cause the sensor glucose sensitivity to change over time, or “drift.” [00226] FIG. 8 is a schematic illustration of a circuit 800 that represents the behavior of an example analyte sensor, such as the analyte sensor 634 shown in FIGS. 6-7. As described above, the interaction of hydrogen peroxide (generated from the interaction between glucose analyte and glucose oxidase) and working electrode (WE) 804 produces a voltage differential between the working electrode (WE) 804 and reference electrode (RE) 806 which drives a current. The current may make up all or part of a raw sensor signal that is measured by sensor electronics, such as the sensor electronics 106 of FIGS. 1-2, and used to estimate an analyte concentration (e.g., glucose concentration). [00227] The circuit 800 also includes a double-layer capacitance (Cdl) 808, which occurs at an interface between the working electrode (WE) 804 and the adjacent membrane (not shown in FIG. 8, see, e.g., FIGS. 6-7 above). The double-layer capacitance (Cdl) may occur at an interface between the working electrode 804 and the adjacent membrane due to the presence of two layers of ions with opposing polarity, as may occur during application of an applied voltage between the working electrode 804 and reference electrode. The equivalent circuit 800 may also include a polarization Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 resistance (Rpol) 810, which may be relatively large, and may be modeled, for example, as a static value (e.g., 100 mega-Ohms), or as a variable quantity that varies as a function of glucose concentration level. [00228] An estimated analyte concentration may be determined from a raw sensor signal based upon a measured current (or charge flow) through the analyte sensor membrane 812 when a bias potential is applied to the sensor circuit 800. For example, sensor electronics or another suitable computing device can use the raw sensor signal and a sensitivity of the sensor, which correlates a detected current flow to a glucose concentration level, to generate the estimated analyte concentration. [00229] With reference to the equivalent circuit 800, when a voltage is applied across the working and reference electrodes 804 and 806, a current may be considered to flow (forward or backward depending on polarity) through the internal electronics of transmitter (represented by R_Tx_internal) 811; through the reference electrode (RE) 806 and working electrode (WE) 804, which may be designed to have a relatively low resistance; and through the sensor membrane 812 (Rmembr, which is relatively small). Depending on the state of the circuit, current may also flow through, or into, the relatively large polarization resistance 810 (which is indicated as a fixed resistance but may also be a variable resistance that varies with the body’s glucose level, where a higher glucose level provides a smaller polarization resistance), or into the double-layer capacitance 808 (i.e., to charge the double-layer membrane capacitor formed at the working electrode 804), or both. [00230] The impedance (or conductance) of the membrane (Rmembr) 812 is related to electrolyte mobility in the membrane, which is in turn related to glucose diffusivity in the membrane. As the impedance goes down (i.e., conductance goes up, as electrolyte mobility in the membrane 812 goes up), the glucose sensitivity goes up (i.e., a higher glucose sensitivity means that a particular glucose concentration will produce a larger signal in the form of more current or charge flow). Impedance, glucose diffusivity, and glucose sensitivity are further described in U.S. Patent Publication No. US2012/0262298, which is incorporated by reference in its entirety. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00231] FIG. 9 is a diagram showing one example of a workflow 900 that may be executed by sensor electronics 106 to generate an estimated analyte concentration 926. A raw sensor signal 916 is sampled, with samples of the raw sensor signal 916 being provided to a Kalman filter 902. The Kalman filter 902 may smooth the incoming raw sensor signal 916 and, in some examples, may reduce or attenuate noise in the raw sensor signal 916. The outputs of the Kalman filter 902 may include a state of the raw sensor signal 916, represented in FIG. 9 as a filtered sensor signal 920, and a rate-of- change of the raw sensor signal 916, represented in FIG. 9 as the ROC 927. The ROC signal may indicate a current rate of change of the raw sensor signal 916 associated with the most recent sample of the raw sensor signal 916 provided to the Kalman filter 902. The ROC signal may indicate a rate of change of the raw sensor signal 916 with or without temperature compensation or a rate of change of the EAVs. In some examples, each sample of the raw sensor signal 916 is converted to a corresponding filtered sensor signal 920 value that depends on previous samples input to the Kalman filter 902. [00232] The filtered sensor signal 920 generated by the Kalman filter 902 is provided to a temperature compensation block 910. At the temperature compensation block 910, the sensor electronics 106 may apply temperature compensation to the filtered sensor 920. The temperature compensation applied at block 910 may correct the sensor signal (e.g., the filtered sensor signal 920) for changes in the behavior of the analyte sensor due to temperature. For example, temperature can influence the catalytic rate of the immobilized enzyme at the membrane as well as, for example, because structural and/or morphological changes to the analyte sensor membrane. Execution of the temperature compensation block 910 may be based on the filtered sensor 920, the ROC 927 generated by the Kalman filter 902, and a temperature of the analyte sensor such as, for example, a working electrode temperature 922. [00233] In some examples, the working electrode temperature 922 may be measured by a temperature sensor at the working electrode of the analyte sensor. In other examples, the working electrode temperature 922 is derived from a temperature sensor at another location. In the example of FIG. 9, a Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 transmitter temperature 918 is captured by a temperature sensor positioned at or near the sensor electronics and, for example, at the surface of the host’s skin. Samples of the transmitter temperature signal may be provided to a linear time invariant (LTI) filter 904. The LTI filter 904 may output an approximation of the working electrode temperature 922 based on the transmitter temperature 918. [00234] The output of the temperature compensation block 910 may be a temperature-compensated sensor signal 928 which is provided to an estimated analyte concentration (EAC) conversion block 912. At the EAC block 912, the sensor electronics 106 may convert the temperature corrected sensor signal 928 to an analyte concentration (e.g., estimated analyte concentration value), for example, utilizing the sensitivity of the analyte sensor. The result may be an interstitial analyte concentration (ISAC) 930 describing the analyte concentration at the host’s interstitial region, which is directly measured by the analyte sensor. [00235] The interstitial analyte concentration is input to a time lag compensation (TLC) block 914. At the block 914, the sensor electronics 106 may convert the interstitial analyte concentration to a blood analyte concentration (BAC) 926. In some examples, the sensor electronics may apply the TLC block 914 to determine the estimated BAC by adjusting an estimated interstitial analyte concentration (e.g., the input to the block 914), such as by applying a correction based on the ROC 924, PH, and a trend (e.g., direction or sign) of the ROC describing the estimated interstitial analyte concentration. For example, the correction may be applied to the estimated interstitial analyte concentration by adding a factor determined by multiplying the ROC, PH and trend. The ROC 924 may be provided to the TLC block 914 by the temperature compensation block 910 and/or may be received directly from the Kalman filter 902. For example, the ROC 927 may be provided directly to the TLC block 914. As described herein, PH is a prediction horizon describing a physiological time lag between blood analyte concentration and interstitial analyte concentration. [00236] In an example related to blood glucose (though other analyte concentrations may be similarly determined), a time-lag compensated Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 estimated glucose value (also referred to herein as an adjusted EGV or a final EGV) may be determined (e.g., by the sensor electronics 106, the processor 210, etc.) using an adaptive PH model. To determine the time-lag compensated estimated glucose value, an estimated glucose value (representing an estimated blood glucose value in the interstitial fluid determined based at least on the raw signal, temperature compensation, a fixed time lag compensation factor, and/or the like) may be adjusted to account for the time lag between blood and interstitial fluid. For example, the estimated glucose value may be adjusted by applying a correction factor dependent on ROC (e.g., the rate-of-change, including direction or sign for the ROC, and PH (e.g., a time lag between blood analyte concentration and interstitial analyte concentration). The correction factor may be determined by multiplying the PH and ROC, among other configurations. The correction factor (which is an adaptive function based at least on PH and ROC provides a bias term that is added to the estimated EGVs to obtain the time-lag compensated EGV. Accordingly, the adaptive PH model according to the present subject matter may determine glucose concentration values (e.g., estimated values of glucose concentration levels) with increased accuracy, computing efficiency, and speed. The adaptive PH model according to the present subject matter may additionally, and/or alternatively, reduce the required computing resources associated with monitoring and/or processing a large quantity of data associated with accounting for the sensor-related conditions and/or environmental factors. [00237] In certain systems, a fixed time lag (PH), given in minutes for example, is assumed and a constant time lag compensation (TLC) is applied. A known algorithm currently implements a static PH of about 7 minutes. The adaptive PH model according to the present subject matter dynamically adjusts for a time lag between blood glucose and interstitial glucose readings. PH modeling suggests that PH is dependent on eTime which refers to the time since insertion. Thus, PH may be adjusted based on eTime. In this example, the correction factor may be a function of PH, ROC, and/or eTime such that the correction factor dynamically adjusts the initially determined estimated glucose value to account for PH, ROC, and/or eTime, and in some examples, along with other factors. In another example, the Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 correction factor is a function of PH, ROC, and/or temperature (e.g., working electrode temperature). In this example, the correction factor may be a function of PH, ROC, eTime, and/or temperature such that the correction factor dynamically adjusts the initially determined estimated glucose value to account for PH, ROC, and/or eTime, and in some examples, along with other factors. [00238] FIG. 10 illustrates, by way of example and not limitation, a dynamically adjusted PH 1000 based on eTime. Several events occur at or near the time of sensor insertion and one or more of these events may be used estimate the time after insertion. By way of example and not limitation, a time of sensor insertion may be estimated based on when a transmitter wakes up after storage. For example, a mechanical magnet may be used to wake up the transmitter. As illustrated in FIG. 10, more time lag compensation (e.g., above the static PH 1050 of about 7 minutes) should occur during the first day after time since insertion. For example, an initial PH 1051 of about 7.5 minutes may be applied during the first day after insertion. For example, a constant PH of about 7.5 may be applied during the first 15 to 20 hours after insertion, after which the PH may transition to a second constant PH 1052 below 7. For example, a constant PH of about 5.5 may be applied after the first day until the end of the sensor. A transition period 1053 may occur between the time with the initial PH 1051 and the time with the second PH 1052. The dynamically adjusted PH 1000 is illustrated with two periods with static PH 1051 and 1052. Thus, the PH may be dynamically adjusted such that the correction factor is different depending on the time after insertion. More complex functions may be implemented to make further adjustments to the PH based on the estimated time since insertion. [00239] FIG. 11 illustrates, by way of example and not limitation, a dynamically adjusted correction factor 1100 based on temperature. As PH modeling also suggests that PH is dependent on temperature at a Twe. Thus, PH may be adjusted based on an actual or estimated Twe. For example, the correction factor decreases with increasing temperature as illustrated in FIG. 11. In another example, the PH may be adjusted based on an actual or estimated temperature of the host. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00240] Both estimated time after insertion and an actual or estimated working electrode temperature may be used to dynamically determine the PH used to determine time-lag compensated (TLC) estimated glucose values (EGV_TLC). EGV_TLC may be determined based on an EGV and adding a factor determined by multiplying ROC and the PH. [00241] PH corresponds to time for blood glucose to transition from a bloodstream of a host into interstitial fluid of the host. PH may be a function of eTime (PH(eTime), where PH decreases with elapsed time. The PH may exponentially decrease with time. For example, PH, as a function of eTime, _{may be illustrated by Equation 1: ^^^^^^^^^^^^^^^^ = ^^^ 1 + ^^ఈభ ௫ ^்^^^ା ఉభ + ^^^.} [1] Although these the relationships may sensors measure [00242] PH may be a function of Twe (PH(Twe), where PH decreases with increasing temperature. The PH may exponentially decrease with increasing _{Twe. as illustrated by Equation 2 (note a2 may be negative): ^^^^^^^^^^^^ = ^^ଶ ∗ ^^^ఈమ ௫ ்௪^ାఉమ^ + ^^ଶ.} [2] [00243] The to indicate whether the variable is for Equation 1 or 2, respectively as the separate equations will have different values for these variables. The adaptive or dynamic PH may be a function of both the PH that is a function of eTime and the PH that is a function of Twe. For example, this function _{may be represented by Equation 3: ^^^^^^^^^^^^^^^,^^^^^^^ = ^^^^^^^^^^^^^^^^ ∗ ^^^^^^^^^^^^.} [3] [00244] The Adaptive PH may be adjusted based on estimated time of insertion and/or temperature. Other inputs like glucose, rate of change, and the like may be included in the adaptive PH function. However, temperature and estimated time from insertion appear to provide better performance improvement. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00245] FIG. 12 illustrate relationships between MARD, as a percentage, and working electrode temperature for the fixed PH and the adaptive PH and between MARD, as a percentage, and a time corresponding to a wear period for a sensor. MARD reflects a difference from a “gold” standard technique or gold reference for accurately detecting blood glucose values. The gold reference may be blood glucose sensing or some other accurate means for detecting blood glucose. As the difference is an absolute difference, MARD does not distinguish whether the estimated blood glucose value derived from the measurement taken from in interstitial fluid is larger or smaller than the tested blood glucose. The adaptive PH consistently reduces MARD for all temperatures. As illustrated at 1254, the MARD reduction is largest at lower temperatures (e.g., 32 to 34 C). Thus, the adaptive PH reduces temperature dependency. The adaptive PH also shows a relatively small but consistent MARD reduction across the ten-day wear period as illustrated at 1255. [00246] FIG. 13 illustrates a comparison of EGV (mg/dL) for a fixed PH and for adaptive PH. Also illustrated, with the black dots, are the actual blood glucose values. The figures are based on clinical data. After about 0.71 of the first day, it can be seen that during the time period 1360 the fixed PH is lower than the reference blood glucose values. The EGV has a higher compensation during this time period 1360 in the first day, and thus the EGV determined using the adaptive PH is closer to the reference blood glucose values compared to the EGVs determined using the fixed PH 1362 during the first day. The EGV determined using adaptive PH 1361 has a lower compensation during time period 1363 of the fourth day, and thus is closer to the reference blood glucose values for compared to EGV determined using the fixed PH 1362 during the fourth day. The illustrated plots in FIG. 13 for the EGV determined using the adaptive PH also reflect that a PSD is used. [00247] Thus, an adaptive time lag compensation model may use time (e.g., time since insertion) and temperature (e.g., working electrode temperature), and/or other factors to dynamically change PH. This results in a consistent MARD reduction across the wear period and across all working electrode temperatures. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00248] FIGS. 14A-14C illustrate examples of functions for adjusting the estimated glucose values (EGVs) based on input(s) to determine an adjusted or final EGV (EGVfinal). Various input(s) may be used, such as EGV, ROC, Outlier Probability, Twe, eTime and CalCheck (CC). Outlier Probability is a metric or measurement that is calculated in the algorithm for checking if a raw signal is an outlier or not. For example, high noise may cause a signal to be an outlier. An algorithm is used to calculate probability for each point. CalCheck refers to a slope (e.g., calibration curve) determined for a particular sensor, or for a population of sensors. The sensor may be calibrated during or after manufacture to provide a predictable analyte response curve that reflects changes to the current generated in response to exposure to a concentration of analyte (e.g., glucose) over time. The functions provide a bias that can be added to the EGV to determine EGVfinal. The bias is just what is to be added to EGV to make a final EGV. The final EGV is the EGV plus the bias (e.g., a correction factor). [00249] The function(s) applied to the input(s) provide a sensitivity correction to adjust EGVs to generate a final EGV (EGVfinal). Current sensitivity algorithms do not consider any linear or nonlinear dependency on glucose. However, it has been observed that EGV has a significant effect on the change in the EGV. [00250] FIG. 14A illustrates a system 1400A that receives an EGV as an input, and applies a function using received EGV input to determine a bias to be summed to EGV to generate the adjusted EGV (or EGVfinal). Input constraints 1464A are applied to the received EGV input. In some examples, EGV has a more significant impact on the change to EGV than other inputs such as Twe, ROC, eTime, Outlier Probability or CalCheck. Thus, the function may be simplified and processing bandwidth saved (with reduced computing requirements, etc.) by using only these four inputs. Though in yet other examples, less inputs or other combinations of inputs may be used. [00251] If the constraints are satisfied, the function (f(EGV)) 1465A may be applied to the input to determine EGVfinal. By way of example and not limitation, a piecewise function may be applied. For example, the constraints may determine that the EGV is neither too high nor too low for the function Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 (or a portion of a piecewise function) to be applied. For example, a first function may be applied when EGV is between w and x, another function may be applied when EGV is between x and y, and another function may be applied when EGV is between y and z. [00252] More than one input may be used to adjust the EGV into the EGVfinal. FIG. 14B illustrates a system 1400B in which four inputs are used to adjust EGV. The illustrated four inputs are EGV, Twe, ROC and Time. The input constraints 1464B may control which function is applied or when a particular function is applied. By way of example and not limitation, the function 1465B may correspond to a piecewise function. It has been observed that each of these inputs have a more significant impact on the change to EGV than other inputs such as Outlier Probability or CalCheck. Thus, the function 1465B may be simplified and processing bandwidth saved (with reduced computing requirements, etc.) by using only these four inputs. Though in yet other examples, less inputs or other combinations of inputs may be used. [00253] The function may correspond to a polynomial. For example, a first- degree polynomial with variables S and B (S x EGV + B) may be used for the entire function or for a portion of a piecewise function. FIG. 14C illustrates a system 1400C in which four inputs (e.g., EGV, Twe, ROC and eTime) are used to adjust EGV by outputting two sub-functions (e.g., the variables S and B for the polynomial function). The function may correspond to more complex polynomials. The illustrated polynomial is provided as an example of a model. Other models may be developed to determine an adjusted or final EGV by more accurately adjusting the sensitivity (instead of adjusting the later determined EGV and/or in addition to later adjusting the determined EGV) of the sensor by adding a bias to raw EGV. The inputs may have constraints 1464C applied as shown. The function may include function(s) 1466C to determine the variables S and B which are input into the polynomial function 1467C. [00254] The function may be found using a neural network and a trained neural network may be used within the system to provide EGVfinal. A neural network may also be used to derive the functions, f and g, 1466C. A Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 neural network may be trained by minimizing a difference between predicted and target values in data. For example, weights may be applied to the input values for each neuron and predictions may be calculated using these initial values. A cost function may be used to measure the error of the neural network and the weights may be updated to reduce or minimize the error using an optimization function. Neural networks are examples of machine learning which may be used to develop models and algorithms that enable computers to learn from data and thus provide a trained model. Other types of machine learning models include supervised machine learning or unsupervised machine learning. Supervised learning may include classification or regression algorithms. Unsupervised algorithms may include clustering or association algorithms. Semi-supervised machine learning may use both labelled and unlabelled data in the training. Reinforcement learning may use trial, error and delay to increase performance using reward feedback. [00255] FIG. 15 illustrates results after performing a search for a bias term for summing to EGV to minimize MARD. The search was performed for different temperatures. It is noted that temperature may be one of the inputs into a function to determine a bias for adjusting EGVs. The chart 1567 illustrates the 5 to 95 percentile range. The resulting trace for each temperature was similar as illustrated in chart 1568 by the trace 1569 which illustrates a bias value for a given EGV that may be added to the EGV to minimize the MARD. The trace 1569, or bias curve, may be modeled as a polynomial function, a piecewise function, a single function or a neural network. MARD was reduced by about 0.2 (see delta MARD plot 1570). Greater reduction in the delta MARD occurs for lower EGV values (e.g., less than 50 and larger EGV values (e.g., greater than 350). Clinical data shows a MARD decrease from 8.43 to 8.20, and a subset of the clinical data showed a MARD reduction from 7.97 to 7.77. The percent match-pairs plot 1571 shows a percent of matched pairs that exists in a particular bin. [00256] Piecewise functions may be used to approximate the trace 1569. For example, different functions may be applied for hypoglycemic values 1572, euglycemic values 1573, and hyperglycemic values 1574 for the EGV. The Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 plots are for different temperatures and for EGVs determined using adaptive PH and PSD. [00257] For hypoglycemic EGVs 1572, the input constraint may be that the EGVs are less than 70. Further, by way of example and not limitation, the system may be designed to receive at least one EGV value (around the under 55 value) within 15 minutes if there is an EGV under 55 and the output of the neural network, if over 55, is averaged with the value under 55. The bias for this hypoglycemic range may be determined as a function of EGV (e.g., f(EGV)), and more particularly may be a first linear function of EGV. For example, the bias for this hypoglycemic range may be determined by subtracting a fraction of EGV from a constant. For example, the bias for this _{range of EGVs may be provided by Equation 4. ^^^^^^^^ ^^^^^^^^ = X − Y ∗ EGV.} [4] [00258] The range for X may be between about 10 and about 20, and the range for Y may be between about 10% to about 40%. For example, X may be about 16 and Y may be about 25%. For the clinical data used in the hypoglycemic model, R² = 89.75%. The coefficient of determination (R²) is a statistical measure that determines how well the data fit the regression model (e.g., proportion of variance in the dependent variable that can be explained by the independent variable). [00259] For euglycemic EGVs 1573, the input constraint may be that the EGVs are between 70 and 180. The bias for this euglycemic range may be determined as a constant (e.g., X) or as a function of EGV (e.g., f(EGV). More particularly, the function for euglycemic EGVs may be a second linear function of EGV. For example, the bias for this euglycemic range may be determined by subtracting a fraction of the EGV (Y*EVG) from a constant _{(X). ^^^^^^^^^^ ^^^^^^^^ = X − Y ∗ ^^^^^^.} [5] The range for X may be between about 0 and 3 and the range for Y may be between about 0.1% and 2%. The range for X may be between 1 and 2 and the range for Y may be below 1%. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00260] For example, the fraction may be about 0.5% (0.005) and the constant may be about 1.2. For example, the bias for this range of EGVs may be provided by Equation 5. For hyperglycemic EGVs, the input constraint is that the EGVs are over 180. The bias for this hyperglycemic range may be determined using an exponential function of EGV (e.g., A^f(EGV)) or an exponential function of EGV and a multiplication factor µ for EGV (e.g., A^{f(EGV, µ )}. For example, the bias for this range of EGVs may be provided by _{Equation 6. (ாீ^ିఓ)మ ^^^^^^^^^^ ^^^^^^^^ = ^^ ଶఙమ .} [6] [00261] The ranges for A, µ and σ may be between about 9 to about 12, between about 200 to about 300, and between about 20 to about 30, respectively. The ranges for A, µ and σ may be between about 10 to about 11, between about 250 to about 300, and between about 24 to about 25, respectively. [00262] FIG. 16 illustrates a median relative difference (RD) compensation to generate a corrected EGV using adaptive PH and a PSD for different temperatures. The range of temperatures is 33.0°C to 37.5°C at 0.5°C intervals. The traces for and highest temperature (37.5°C) 1675 and the lowest temperature (33.0°C) 1676 are observed to have the highest median relative difference compensation and also appear to have the largest swings in the RD compensation. Also illustrated are traces for the second lowest temperature (33.5°C) 1677 and the second highest temperature (37.0°C) 1678. The traces for the remainder of the temperatures between 33.5 and 37.0 C appear to more closely follow similar shapes and thus are illustrated as the shaded shape 1679. These temperatures appear to have a similar bias for minimizing MARD. The traces for the different temperatures are more similar at lower EGVs (e.g., less than 200), and have larger variation for higher EGVs. RD compensation may use temperature as an input. [00263] FIG. 17 illustrates a neural network as an example of a model that may be used to determine variables of a polynomial (e.g., S x EGV + B) based on inputs EGV, Twe, ROC and eTime. That is, the neural network 1700 may be used to fit a function like a polynomial. A fitted polynomial Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 function may be a simpler function than a neural network, yet be effective for adjusting sensitivity to get a more accurate final EGV. There are some constraints or limitation for the inputs into the neural network. For example, the input should be between 40 and 400 for EGV and the value for Twe should be between about 31 and about 38. Any number outside of those values cap, for example. Thus, for example, a lower temperature will pass to 31 C. In furtherance of the example, ROC may be bounded between negative 2.45 to 2.45 and eTime is bounded between 0 and 10.5. For example, any number over 10.5 would be capped at 10.5. The neural network is not applied for values outside of the range. The neural network may work on the instance time (or current time), but also may be designed with inputs from previous time(s) and thus use a history of these variables as variables (e.g., recursive neural network). [00264] The illustrated neural network includes inputs 1777 (e.g., four inputs for EGV, Twe, ROC, and eTime). The four input nodes are connected to 32 nodes within the hidden layers of the illustrated neural network. An activation function (RELU) is applied. RELU passes positive values and passes a 0 for negative numbers to 16 nodes within the hidden layers 1778. The activation function (RELU) may be applied to pass values to the output layer. The output layer 1779 may be used to determine S and B for the polynomial. This is but one example of a neural network model. Other types of neural network architecture may be designed to determine the bias to be applied to the EVGs to minimize the MARD. [00265] FIG. 18 illustrates provides a comparison between performance without relative difference compensation and performance with relative difference compensation. The plotted EGVs are provided based on EGVs compensated using adaptive PH and PSD. The charts illustrate bins for different EGVs, including a 40-60 bin, a 60-80 bin, an 80-120 bin, a 120-160 bin, a 160-200 bin, a 200-250 bin, a 230-300 bin and a 300+400 bin. As illustrated by chart 1880, the MARD was reduced for all EGV bins with largest reductions in the 60-80 bin and the 230-300 bin. The standard deviation of the relative difference (SDRD) chart 1881 shows a slight reduction in the SDRD for some but not all bins. The largest reduction in SDRD was found in the 60-80 bin and the 80-120 bin. The mean relative Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 difference (MRD) chart 2077 shows a relatively large reduction in the mean relative difference for the 40-60 bin. The chart 1882 also shows a fairly large reduction in the mean relative difference for the 200-250 and 23-300 bins, and shows slight increases in the mean relative difference for the 60-80, 80- 120, 160-200 and 300-400 bins. [00266] FIG. 19 illustrates a comparison of correction methods for minimizing a relative difference. The figure illustrates a hypo compensation method for hypoglycemic EGVs 1983, an EGV bin bias compensation method 1984 such as using the mean relative differences shown in FIG. 18, and a neural network relative difference compensation 1985. As illustrated in the figure, the neural network correction method allows sensitivity to be adjusted in different ways for EGVs below 70 (e.g., hypoglycemic values) where the difference is positive, EGVs between 70 and 180 (e.g., euglycemic values) where the difference is negative, and EGVs above 180 (e.g., hyperglycemic values) where the difference is positive. [00267] FIG. 20 illustrates a relationship between an EGV multiplication factor (µ) for different inputs such as EGVs, Twe, ROC and PH, eTime, and sensitivity. The multiplication factor µ has an exponential relationship for smaller values of EGVs (e.g., hypoglycemic range). The data suggests that sensitivity has an exponential dependency to glucose. [00268] Sensitivity is a function of time and is the slope (m) of a measured raw sensor current to an analyte concentration. M(t) represents the change in the sensitivity and is a function of time and a fixed value from CalCheck, which is determined based on testing of different batches of sensors. [00269] The neural network suggests that glucose concentration affects the sensitivity of the sensor. Thus, the slope (m) (e.g., “sensitivity”) of a measured raw sensor current to the analyte concentration should depend on time, CalCheck and glucose concentration (output of the NN suggestion). Thus, functions may be created to calculate the value for the corrected glucose values based on a relationship of glucose, time and CalCheck. So, instead of determining an EGV and then correcting the EGV with a bias, a system may be designed to more accurately determine the EGV based on a Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 raw glucose level, time and CalCheck. Additional inputs may be used in the calculation, such as ROC, Twe, eTime, and/or outlier probability. [00270] FIG. 21 illustrates a plot of data representing a bias against EGVs and further illustrates a fit for the data. The fit indicates that a positive bias may be added to the raw EGVs below about 80 and that a negative bias may be added for raw EGVs above about 300. Thus, the illustrated fit accounts for the effect that the raw EGVs have on the bias and thus provides more accurate adjusted or final EGVs. [00271] FIG. 22 illustrates an EGV average offset for different bins of EGV ranges and further illustrates the EGV average offset for a compensation technique that uses PH and PSD and for another compensation technique that uses a neural network with the PH and PSD. Thus, the dependency of MRD to EGV ranges is significantly reduced after the with the addition of the NN, which enhances the accuracy of the EGVs. As illustrated in the figure, the reduction in MRD is largest for low EGVs (e.g., lower than 60) and high EGVs (e.g., over 200). [00272] Thus, a model such as a computerized model (e.g., a machine learning model, neural network, etc.) may be implemented to improve the accuracy of EGVs. For example, a correction factor may be applied to calculate a more accurate EGV. The correction factor can be a function of the EGV (calculated using the current approach), Twe, eTime, and ROC. The correction factor may be derived by the model using the EGV (calculated using the current approach), Twe, eTime, and ROC as inputs. Additionally, and/or alternatively, the model (such as but not limited to a neural network) may be used to update the sensitivity value in EGV calculations. In this example, the model can be used to determine a sensitivity value based on Twe, eTime, and ROC, among other parameters. [00273] The present subject matter discusses glucose sensors as a specific analyte sensor. The present subject matter may be implemented for analyte sensing systems. Thus, for example, the term estimated analyte value (EAV) may be substituted for the term estimated glucose value (EGV). Estimated values of other analytes (e.g., lactate, ketones, etc.) may similarly be substituted for the term EAV, EGV, etc. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00274] Thus, the model according to the present subject matter may determine analyte concentration values (e.g., estimated values of analyte concentration levels) and/or sensitivity values with increased accuracy, computing efficiency, and speed. The model according to the present subject matter may additionally, and/or alternatively, reduce the required computing resources associated with monitoring and/or processing a large quantity of data associated with accounting for the described inputs. [00275] A continued goal is to further improve MARD, which is the Mean Absolute Relative Difference from a gold standard for determining the analyte level. Various embodiments provide a model that incorporates historical information to generate the estimated analyte values, such as EGV. The model shown in FIG. 23 further improves the MARD compared to the model shown in FIG. 17. Both models are trained on a dataset used for development, and the trained parameters can be fixed and transferred for implementation in the sensor electronics. [00276] Accurately estimating glucose levels from interstitial sensor readings in CGM devices is important for effective diabetes management. However, behaviors not captured by models can complicate accurate glucose prediction. The neural network for the CGM model illustrated in FIG. 17 adjusts for these non-modeled behaviors. It uses inputs such as eTime, temperature, rate of change (ROC), and EGV to produce a corrected EGV. This model can be further improved, as shown in FIG. 23, by incorporating historical data and/or additional signals into the neural network to boost CGM prediction accuracy. Using historical data helps the neural network identify trends and patterns that are not visible from real-time data alone. [00277] FIG. 23 illustrates a neural network as an example of a model that may be used to determine a correction factor for an algorithm used to determine an estimated analyte value. The correction factor p can be applied to the sensitivity (M(t)) that is used for EGV estimation instead of being applied to the estimated EGV at the EGV level. The sensitivity may be referred to as a predicted sensitivity, where a change in sensitivity may be a function of time. However, the system may be configured to determine and Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 apply the correction factor to the estimated EGV to determine an EGV that is provided to the host at the EGV level. [00278] The trained neural network may take as inputs current sensor parameters and historical data to produce corrected analyte values (e.g., EGVs). For example, historical data input into the neural network may include historical filtered data. The filtered data can be divided by sensor sensitivity (M(t)) and/or current temperature of the working electrode (Twe). The filtered data can be generated based on a filtered signal, which refers to the signal after temperature compensation and Kalman filtering (e.g., via the Kalman filter 902) is applied to the generated signal, or after background noise has been subtracted or removed from the generated signal. Current sensor parameters may include the predicted real-time M(t), the eTime, the ROC of the estimated analyte value, and the prediction horizon. [00279] The neural network model 2300 may include an input layer 2372, three hidden layers 2373, and an output layer 2374. Other configurations are also contemplated. The first hidden layer may have 32 neurons or nodes, the second hidden layer may have 16 neurons or nodes, and the third hidden layer may have 2 neurons or nodes. Each neuron in a layer receives inputs from all neurons in the previous layer, applies a weighted sum and activation function, and passes the result to the next layer. Features input into the neural network model 2300 may include the eTime, the estimated analyte value (e.g., estimated glucose value (EGV)), the rate of change of the estimated analyte value (ROC), and the working electrode temperature (Twe). Historical data, among other parameters, may also be input into the neural network model 2300. [00280] Some embodiments further include new input signals such as noise and residuals measurements. Residuals refer to the difference between filtered and unfiltered signals. Additional inputs include existing CGM model equations like sensitivity (m(t)), background (bkg(t)), and a dip and recovery (DNR(t)) model. Often, when an in vivo continuous analyte sensor is inserted, the sensor may show prolonged dips in measured glucose levels, which eventually return to normal. This phenomenon, known as “dip and recover” (DnR), may result from increased glucose consumption for Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 immediate energy to support the immune response at the insertion site. DnR events can last from a few hours up to several days after insertion, potentially causing false low glucose alerts and inaccurate readings. Models or algorithms can be used to compensate for DnR events (e.g., see US Publication No. 2024/0293054, which is incorporated by reference herein in its entirety). These signals offer the neural network a more complete understanding of the factors affecting glucose levels. [00281] Some embodiments incorporate historical data through moving averages of the data. As an example, a moving average over the last 50 minutes may be used as input to the model. Some embodiments might select a moving average period of about 50 minutes (e.g., 50 minutes +/- 2%, 5%, 8%, 10%, 15%, 20%, or 25%), 30 minutes (e.g., 30 minutes +/- 2%, 5%, 8%, 10%, 15%, 20%, or 25%), 15 minutes (e.g., 15 minutes +/- 2%, 5%, 8%, 10%, 15%, 20%, or 25%), 75 minutes (e.g., 75 minutes +/- 2%, 5%, 8%, 10%, 15%, 20%, or 25%), 90 minutes (e.g., 90 minutes +/- 2%, 5%, 8%, 10%, 15%, 20%, or 25%), etc. The historical data could include, for example, moving averages of the EGV, ROC, residuals, and noise measures fed into the neural network. In some cases, all these moving averages might use the same time period, although different parameters may calculate their moving averages over different durations. [00282] A specific example of the improved model includes the following inputs: a 50-minute moving average of EGV; a 50-minute moving average of the rate of change (ROC); a 50-minute moving average of residual/m(t); and a 50-minute moving average of a noise measure. Additionally, the inputs can continue to include EGV, temperature, ROC, and eTime, which were also fed into the neural network shown in FIG. 17. The inputs may also incorporate sensitivity (m(t)), a background signal (bkg(t)), and DNR(t). Initial testing of the enhanced model demonstrates a significant improvement in accuracy (MARD) and iCGM metrics, indicating more precise glucose level predictions and improved overall performance. While this specific example is described in relation to the 50-minute moving average, other durations may be used as described, such as 30 minute durations, 15 minute durations, 75 minute durations, 90 minute durations, etc. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00283] Some embodiments incorporate historical data using point-wise data instead of moving averages into the neural network model to enhance CGM prediction accuracy. This method may improve prediction by allowing the model to better recognize temporal patterns and physiological dynamics, such as the time lag effect. The point-wise historical data can include discrete samples spaced over time. The point-wise historical data may include a filtered signal and/or sensitivity (e.g., FILTERED/M(T)). As an example, a number of samples may be equally spaced over the course of a period of time such as an immediately-preceding period of time. The period of time may be within the last 10 hours (e.g., the last 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 hours) or may be within the last 5 hours (e.g., the last 30, 60, 90, 120, 150, 180, 210, 240, 270 or 300 minutes). The number of samples may be 25 or less samples (e.g., less than 10 samples) over the period of time. Various examples include a sample every 10, 15, 20, 30, 40, 45, 60, 75 or 90 minutes. For example, three samples over two hours may include a current sample (0 minutes) and two additional points separated by 60-minute intervals (-60 and -120 minutes). An example may include eight samples of data over the last nine hours, equally spaced, which can include the current data point (0 minutes) and seven additional points separated by 90-minute intervals (-90 minutes, -180 minutes, -270 minutes, -360 minutes, -450 minutes, -540 minutes, and -300 minutes). An example may include five samples over the last five hours, equally spaced, which may include the current data point (0 minutes) and four additional points separated by 75- minute intervals (-75 minutes, -150 minutes, -225 minutes, and -300 minutes). FILTERED refers to the raw sensor signal after application to the raw sensor signal of compensation and/or filtering, such as temperature compensation, background subtraction, and Kalman filtering, while m indicates sensor sensitivity. Additionally, three samples of historical working electrode temperature (Twe) may be taken over the sample time period (e.g., across 5 hours at 0 minutes, -150 minutes, and -300 minutes). [00284] Additional signals and inputs include eTime (elapsed time), ROC × PH (rate of change multiplied by prediction horizon), and M(t). The correction factor may be generated by the neural network and used in the EGV algorithm. The correction factor may be used to correct for the Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 sensitivity (M(t)). For example, an EGV algorithm may receive inputs indicative of a progressive sensitivity decline, a baseline (BL), and a dip and recovery (DNR) for the estimated analyte values after implant, a filtered signal, M(t), ROC, and PH. The filtered signal may be adjusted into an adjusted filtered signal using M(t), and a product term (ROC*PH) may be adjusted into an adjusted product term using M(t). The EGV algorithm may include a sum of product terms. One product term in the algorithm may be based on progressive sensitivity decline, the adjusted filtered signal, and the correction factor, and another product term in the algorithm may be based on the adjusted product term for ROC*PH, and the correction factor. An EGV algorithm may sum the BL and/or DNR to the product terms. An EGV algorithm example may include: ^_{^^^^^ ி^^௧^^^ௗ ோை^∗^ு ^^^^^ = ^^^^ + ^^^^^^ + ^^^^^^^^^^ ∗ ெ(௧) ∗ ^^ + ெ(௧) ∗ ^^.} Initial in prediction accuracy, as measured by the MARD and iCGM metrics. These results indicate more reliable glucose level estimates and better overall performance compared to the model shown in FIG. 17. [00285] The amount of data input into the model is determined to prevent overfitting or underfitting. Both issues can lead to poor performance on new data. Underfitting fails to capture the underlying trends or structure in the data, while overfitting captures too much noise or irrelevant details from the training set, hindering the model's ability to identify actual trends. [00286] For example, neural networks used to correct estimated glucose values (EGVs) may have a potential to produce unreliable or extreme corrections when they encounter input combinations that are rare in the training data. For example, neural network-based corrections may be applied directly to the estimated analyte value without any built-in way to limit the size of the correction, but the neural networks may potentially cause large errors in regions of the input space where they have not been sufficiently trained. [00287] FIG. 24A illustrates values for a first algorithm, labeled ALGORITHM A, and a second algorithm, labeled ALGORITHM B, and FIG. 24B illustrates point-wise historical values for ALGORITHM B. For Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 example, ALGORITHM A may correspond to the algorithm in FIG. 17 and ALGORITHM B may correspond to the algorithm in FIG. 23, which includes point-wise historical data from the filtered/M(t), as illustrated in FIG. 24B. As illustrated in FIG. 24A, ALGORITHM B may include an observed artifact attributable to a very low historical raw signal in FIG. 24B because extremes in the data (e.g., very low numbers) may not have been sufficiently tested. [00288] Various embodiments may implement the soft capping mechanism within the sensor electronics to provide a safety guardrail for the neural network, while still maintaining the advantages of neural network-based corrections. For example, the soft capping mechanism might be implemented using a hyperbolic tangent (tanh) function to gently limit the correction applied by the neural network to the legacy EGV. [00289] For example, the sensor electronics may implement the soft capping mechanism using two EGV algorithms. One of the algorithms, referred to as a “legacy” algorithm, is not as susceptible to aberrations. For example, ALGORITHM A in FIG. 24B may be the “legacy” algorithm. A difference between the analyte values determined by the two algorithms (e.g., the EGV determined using the neural network and the legacy EGV determined by the other algorithm): deltaEGV=(egv_nn) − egv_legacy. Soft capping may be applied as: deltaEGV = (nn_correction_cap) * tanh(deltaEGV /nn_correction_cap). The corrected, capped EGV is based on the legacy EGV and the capped correction: egv = (egv_legacy) + (deltaEGV). This soft capping approach ensures that small corrections are preserved, large corrections are smoothly limited without abrupt discontinuities, and the correction remains bounded within a safe and interpretable range. [00290] FIG. 25A illustrates a cumulative probability of the deltaEGV. A guardrail of 25 mg/dl only caps about 0.2% of the corrections from the neural network. Thus, the soft capping limits the extreme corrections. FIG. 25B illustrates a hyperbolic tangent (tanh) function with 25 mg/dl guardrails. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 Guardrails may be based as a percentage (X %). For example, the cap may be set to be about 0.1%, 0.2%, 0.3%, 0.4%, 0.5% 0.6%, 0.7%, 0.8%, 0.9%, 1%, 2%, 3%, 4% or 5%. Guardrails may be based on an absolute value (e.g., 20, 21, 22, 23, 24, 25, 26, 27, 29, or 30 mg/dl). [00291] An adaptive soft capping mechanism may be implemented. Instead of using a fixed correction cap for the neural network, the cap may be dynamically computed as a moving average of the absolute deltaEGV values. For example, the moving average may be calculated over the preceding 1-hour window. The adaptive soft capping mechanism enables the system to increase the cap in stable, well-trained regions to allow more aggressive correction and decrease the cap in volatile or uncertain regions to enforce tighter control. The adaptive cap is itself bounded between predefined minimum and maximum values to ensure safety and prevent overcorrection or underutilization of the neural network. [00292] FIG. 26A illustrates a method for enhancing analyte sensor accuracy. The method may be implemented using sensor electronics 106. The method may include, at 2680, generating estimated analyte values from a raw sensor signal such as raw sensor signal 916 in FIG. 9. At 2681, the method may include maintaining historical data over a predetermined time period, and, at 2682, applying a trained neural network model, such as model 2300 in FIG. 23, that receives as inputs (e.g., inputs 2372 in FIG. 23) current sensor parameters and the historical data to generate corrected analyte values., The method may include, as illustrated at output 2374 in FIG. 23, outputting the corrected analyte values for use in analyte monitoring 2683. The historical data may include moving averages or point- wise historical data, as described above. [00293] FIG. 26B illustrates another method for enhancing analyte sensor accuracy. The method may be implemented using sensor electronics 106. The method may include, at 2690, generating a raw sensor signal such as raw sensor signal 916 in FIG. 9. The raw sensor signal may be associated with an analyte concentration of a host. The method may include, at 2691, generating estimated analyte values from the raw sensor signal and, at 2692, applying a correction, determined by the neural network, such as network Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 2300 in FIG. 23, to the estimated analyte values to generate corrected analyte values. The method may include, at 2693, implementing a soft capping mechanism to limit the magnitude of corrections applied by the neural network and, at 2694, outputting adjusted analyte values based on the soft-capped corrections. The soft capping mechanism may be implemented by the sensor electronics, such as sensor electronics 106 in FIG. 1, and may include a hyperbolic tangent function and adaptive capping, as described above. [00294] FIG. 27 is a block diagram illustrating a computing device hardware architecture 2700, within which a set or sequence of instructions can be executed to cause a machine to perform examples of any one of the methodologies discussed herein. The hardware architecture 2700 can describe various computing devices, including, for example, the sensor electronics 106, the peripheral medical device 122, the smart device 112, the tablet 114, etc. [00295] The architecture 2700 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the architecture 2700 may operate in the capacity of either a server or a client machine in server-client network environments, or it may act as a peer machine in peer-to-peer (or distributed) network environments. The architecture 2700 can be implemented in a personal computer (PC), a tablet PC, a hybrid tablet, a set-top box (STB), a personal digital assistant (PDA), a mobile telephone, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing instructions (sequential or otherwise) that specify operations to be taken by that machine. [00296] The example architecture 2700 includes a processor unit 2702 comprising at least one processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both, processor cores, compute nodes). The architecture 2700 may further comprise a main memory 2704 and a static memory 2706, which communicate with each other via a link 2708 (e.g., bus). The architecture 2700 can further include a video display unit 2710, an input device 2712 (e.g., a keyboard), and a UI navigation device 2714 (e.g., a mouse). In some examples, the video display unit 2710, input Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 device 2712, and UI navigation device 2714 are incorporated into a touchscreen display. The architecture 2700 may additionally include a storage device 2716 (e.g., a drive unit), a signal generation device 2718 (e.g., a speaker), a network interface device 2720, and one or more sensors (not shown), such as a Global Positioning System (GPS) sensor, compass, accelerometer, or another sensor. [00297] In some examples, the processor unit 2702 or another suitable hardware component may support a hardware interrupt. In response to a hardware interrupt, the processor unit 2702 may pause its processing and execute an ISR, for example, as described herein. [00298] The storage device 2716 includes a machine-readable medium 2722 on which is stored one or more sets of data structures and instructions 2724 (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. The instructions 2724 can also reside, completely or at least partially, within the main memory 2704, within the static memory 2706, and/or within the processor unit 2702 during execution thereof by the architecture 2700, with the main memory 2704, the static memory 2706, and the processor unit 2702 also constituting machine- readable media. EXECUTABLE INSTRUCTIONS AND MACHINE-STORAGE MEDIUM [00299] The various memories (i.e., 2704, 2706, and/or memory of the processor unit(s) 2702) and/or storage device 2716 may store one or more sets of instructions and data structures (e.g., instructions) 2724 embodying or used by any one or more of the methodologies or functions described herein. These instructions, when executed by processor unit(s) 2702 cause various operations to implement the disclosed examples. [00300] As used herein, the terms “machine-storage medium,” “device- storage medium,” “computer-storage medium” (referred to collectively as “machine-storage medium 2722”) mean the same thing and may be used interchangeably in this disclosure. The terms refer to a single or multiple storage devices and/or media (e.g., a centralized or distributed database, and/or associated caches and servers) that store executable instructions Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 and/or data, as well as cloud-based storage systems or storage networks that include multiple storage apparatus or devices. The terms shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media, and/or device-storage media 2722 include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms machine-storage media, computer-storage media, and device-storage media 2722 specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium” discussed below. SIGNAL MEDIUM [00301] The term “signal medium” or “transmission medium” shall be taken to include any form of modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. COMPUTER-READABLE MEDIUM [00302] The terms “machine-readable medium,” “computer-readable medium” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. The terms are defined to include both machine-storage media and signal media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. [00303] The instructions 2724 can further be transmitted or received over a communications network 2726 using a transmission medium via the network interface device 2720 using any one of a number of well-known transfer protocols (e.g., HTTP). Examples of communication networks include a Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 LAN, a WAN, the Internet, mobile telephone networks, plain old telephone service (POTS) networks, and wireless data networks (e.g., Wi-Fi, 3G, 4G LTE/LTE-A, 5G or WiMAX networks). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible media to facilitate communication of such software. [00304] Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein. [00305] Various components are described in the present disclosure as being configured in a particular way. A component may be configured in any suitable manner. For example, a component that is or that includes a computing device may be configured with suitable software instructions that program the computing device. A component may also be configured by virtue of its hardware arrangement or in any other suitable manner. [00306] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) can be used in combination with others. Other examples can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is to allow the reader to quickly ascertain the nature of the technical disclosure, for example, to comply with 37 C.F.R. §1.72(b) in the United States of America. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 [00307] Also, in the above Detailed Description, various features can be grouped together to streamline the disclosure. However, the claims cannot set forth every feature disclosed herein, as examples can feature a subset of said features. Further, examples can include fewer features than those disclosed in a particular example. Thus, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate example. The scope of the examples disclosed herein is to be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. [00308] Each of these non-limiting examples in any portion of the above description may stand on its own or may be combined in various permutations or combinations with one or more of the other examples. [00309] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the subject matter can be practiced. These embodiments are also referred to herein as “examples.” Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein. [00310] In the event of inconsistent usages between this document and any documents so incorporated by reference, the usage in this document controls. [00311] In this document, the terms “a” or “an” are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of “at least one” or “one or more.” In this document, the term “or” is used to refer to a nonexclusive or, such that “A or B” includes “A but not B,” “B but not A,” and “A and B,” unless otherwise indicated. In this document, the terms “including” and “in which” are used as the plain- English equivalents of the respective terms “comprising” and “wherein.” Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 Also, in the following claims, the terms “including” and “comprising” are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms “first,” “second,” “third,” etc., are used merely as labels, and are not intended to impose numerical requirements on their objects. [00312] Geometric terms, such as “parallel”, “perpendicular”, “round”, or “square” are not intended to require absolute mathematical precision, unless the context indicates otherwise. Instead, such geometric terms allow for variations due to manufacturing or equivalent functions. For example, if an element is described as “round” or “generally round”, a component that is not precisely circular (e.g., one that is slightly oblong or is a many-sided polygon) is still encompassed by this description. [00313] Method examples described herein can be machine or computer- implemented at least in part. Some examples can include a computer- readable medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer- readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like. [00314] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the Detailed Description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the subject matter should be determined with reference to the claims, along with the full scope of equivalents to which such claims are entitled.

Claims

Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 CLAIMS What is claimed is: 1. An analyte sensor system, comprising: an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host; and sensor electronics configured to perform operations comprising: generating estimated analyte values of the analyte concentration based at least on the raw sensor signal; determining a rate-of-change for the estimated analyte values or the raw signal; determining a prediction horizon as a function of at least one of an elapsed time since sensor insertion and a working electrode temperature of a working electrode of the analyte sensor; and generating a time-lag-compensated estimated analyte value for at least one of the estimated analyte values as a function of at least the determined prediction horizon and the rate-of-change, thereby improving performance of the analyte sensor system; or adjusting the estimated analyte values by applying a correction that is a function of at least the estimated analyte values, thereby improving performance of the analyte sensor system.

2. The analyte sensor system according to claim 1, further comprising: determining the working electrode temperature; and determining the elapsed time since sensor insertion.

3. The analyte sensor system according to claim 2, wherein for a given estimated analyte value the time-lag-compensated estimated analyte value is determined by summing a time-lag bias term to the given estimated analyte value, and the time-lag bias term is determined as a function of the prediction horizon and the rate-of-change.

4. The analyte sensor system according to claim 3, wherein the prediction horizon corresponds to time for blood glucose to transition from a bloodstream Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 of a host into interstitial fluid of the host, and the prediction horizon is a function of the elapsed time since sensor insertion.

5. The analyte sensor system according to any of claims 3-4, wherein the prediction horizon corresponds to time for blood glucose to transition from a bloodstream of a host into interstitial fluid of the host, and the prediction horizon is a function of the working electrode temperature.

6. The analyte sensor system according to any of claims 1-5, wherein the prediction horizon is a function of both the elapsed time since sensor insertion and the working electrode temperature.

7. The analyte sensor system according to any of claims 1-6, wherein the prediction horizon decreases with increasing elapsed time.

8. The analyte sensor system according to any of claims 1-7, wherein the prediction horizon decreases with increasing temperature.

9. The analyte sensor system according to any of claims 1-8, wherein the sensor performance is improved by at least applying the correction.

10. The analyte sensor system according to claim 9, wherein the correction is also a function of the working electrode temperature.

11. The analyte sensor system according to any of claims 9-10, wherein the correction is also a function of the rate-of-change.

12. The analyte sensor system according to any of claims 9-11, wherein the correction is also a function of the elapsed time since sensor insertion.

13. The analyte sensor system according to any of claims 9-12, wherein the correction is also a function of the rate-of-change, the working electrode temperature and the elapsed time since sensor insertion. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

14. The analyte sensor system according to any of claims 1-13, wherein a trained computerized model is used to adjust the estimated analyte values by at least determining the correction.

15. The analyte sensor system according to claim 14, wherein inputs to the trained computerized model include at least one of the estimated analyte values, working electrode temperature, rate-of-change, and elapsed time since sensor insertion.

16. The analyte sensor system according to claim 15, wherein the trained computerized model is a neural network.

17. The analyte sensor system according to any of claims 15-16, wherein: the sensor electronics are configured to determine adjusted the estimated analyte values by multiplying the estimated analyte value by a first function of the estimated analyte value, the working electrode temperature, rate-of-change, and the elapsed time since sensor insertion and adding a second function of estimated analyte value, the working electrode temperature, the rate-of-change, and the elapsed time since sensor insertion.

18. The analyte sensor system according to claim 17, wherein the trained computerized model outputs the first function and the second function based at least on the inputs to the trained computerized model.

19. The analyte sensor system according to claim 18, wherein the correction is applied to improve sensor performance by adding a bias term to the estimated analyte value to minimize a mean absolute relative difference.

20. The analyte sensor system according to claim 19, wherein the bias term is determined by implementing a piecewise curve fitting.

21. The analyte sensor system according to any of claims 14-20, wherein the trained computerized model applies a capping mechanism to limit adjustments to the estimated analyte values. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

22. The analyte sensor system according claim 21, wherein the capping mechanism is applied using a hyperbolic tangent function to limit the adjustments.

23. The analyte sensor system according to claim 22, wherein the capping mechanism is applied according to a formula: deltaEGV = nn_correction_cap * tanh(deltaEGV / nn_correction_cap), where deltaEGV represents a difference between neural network corrected values and legacy estimated analyte values.

24. The analyte sensor system according to any of claims 21-23, wherein the capping mechanism is configured to dynamically adjust a correction cap based on historical correction data.

25. The analyte sensor system according to claim 24, wherein the capping mechanism is configured to determine the correction cap as a moving average of absolute deltaEGV values over a predetermined time window.

26. The analyte sensor system according to any of claims 1-25, wherein the analyte sensor is a glucose sensor, and wherein the analyte concentration is a glucose concentration.

27. A method comprising: generating a raw sensor signal, associated with an analyte concentration of a host, using an analyte sensor, and using sensor electronics to perform operations including generating estimated analyte values of the analyte concentration based at least on the raw sensor signal, determining a rate-of-change for the estimated analyte values or the raw signal, and determining a prediction horizon as a function of an elapsed time since sensor insertion and a working electrode temperature and generating a time-lag-compensated estimated analyte value for at least one of the estimated analyte values as a function of at least the determined prediction horizon and the rate-of-change, thereby improving performance of the analyte sensor. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

28. The method of claim 27, wherein for a given estimated analyte value the time-lag-compensated estimated analyte value is determined by summing a time- lag bias term to the given estimated analyte value, and the time-lag bias term is determined by multiplying the prediction horizon by the rate-of-change.

29. The method of claim 28, wherein the prediction horizon corresponds to time for blood glucose to transition from a bloodstream of a host into interstitial fluid of the host, and the prediction horizon is a function of the elapsed time since sensor insertion.

30. The method of claim 28, wherein the prediction horizon corresponds to time for blood glucose to transition from a bloodstream of a host into interstitial fluid of the host, and the prediction horizon is a function of the working electrode temperature.

31. The method of claim 28, wherein the prediction horizon decreases with increasing elapsed time.

32. The method of claim 28, wherein the prediction horizon decreases with increasing temperature.

33. A method, comprising: generating a raw sensor signal, associated with an analyte concentration of a host, using an analyte sensor, and using sensor electronics to perform operations including generating estimated analyte values from the raw sensor signal and adjusting the estimated analyte values by applying a correction that is at least a function of the estimated analyte values, thereby improving performance of the analyte sensor.

34. The method according to claim 33, wherein the operations performed using the sensor electronics include determining a working electrode temperature, and the correction is also a function of the working electrode temperature. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

35. The method according to any of claims 33-34, wherein the operations performed using the sensor electronics include determining a rate-of-change for the estimated analyte values or the raw signal, and the correction is also a function of the rate-of-change.

36. The method according to any of claims 33-35, wherein the operations performed using the sensor electronics include determining an elapsed time since sensor insertion, and the correction is also a function of the elapsed time since sensor insertion.

37. The method according to claim 33, wherein the operations performed using the sensor electronics include determining a working electrode temperature, a rate-of-change for the estimated analyte values or the raw signal, and an elapsed time since sensor insertion, and the correction is also a function of the working electrode temperature, the rate-of-change, and the elapsed time since sensor insertion.

38. The method according to any of claims 33-37, wherein the estimated analyte values are adjusted by at least determining the correction using a trained computerized model.

39. The method according to claim 38, further comprising receiving inputs to the trained computerized model, wherein the inputs include the estimated analyte values, a working electrode temperature, a rate-of-change, and an elapsed time since sensor insertion.

40. The method according to claim 39, wherein the trained computerized model is a neural network.

41. The method according to any of claims 39-40, further comprising using the sensor electronics to determine adjusted estimated analyte values by multiplying the estimated analyte value by a first function and adding a second function, wherein both the first function and the second function are functions of Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 the estimated analyte value, the working electrode temperature, the rate-of- change, and the elapsed time since sensor insertion.

42. The method according to claim 41, further comprising outputting the first function and the second function from the trained computerized model based on at least the inputs to the trained computerized model.

43. The method according to any of claims 38-42, further comprising using the trained computerized model to apply a capping mechanism to limit adjustments to the estimated analyte values.

44. The method according to claim 43, wherein using the trained computerized model to apply the capping mechanism includes using a hyperbolic tangent function to limit the adjustments.

45. The method according to claim 44, wherein the capping mechanism is applied according toa formula: deltaEGV = nn_correction_cap * tanh(deltaEGV / nn_correction_cap), where deltaEGV represents a difference between neural network corrected values and legacy estimated analyte values.

46. The method according to any of claims 43-45, further comprising using the capping mechanism to dynamically adjust a correction cap based on historical correction data.

47. The method according to claim 46, further comprising using the capping mechanism to determine the correction cap as a moving average of absolute deltaEGV values over a predetermined time window.

48. The method according to any of claims 39-47, wherein receiving inputs to the trained computerized model further includes receiving sensitivity values, background values, and drift and recovery values.

49. The method according to any of claims 33-48, further comprising training a computerized model to determine the correction based on inputs that Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 include the estimated analyte value, a working electrode temperature, a rate-of- change for the estimated analyte values or the raw signal, and an elapsed time since sensor insertion.

50. The method according to any of claims 33-49, wherein the applying the correction includes adding a bias term to the estimated analyte value to minimize a mean absolute relative difference.

51. The method according to claim 50, further comprising implementing piecewise curve fitting to determine the bias term to be added to the estimated analyte value to minimize the mean absolute relative difference.

52. The method according to claim 51, wherein the piecewise curve fitting to determine the bias term includes a hyperglycemic curve fitting for the estimated analyte values considered to be too high, a euglycemic curve fitting for the estimated analyte values considered to be in a normally acceptable range, and a hypoglycemic curve fitting for the estimated analyte values considered to be too low.

53. The method according to claim 52, wherein the bias term is: for estimated analyte values below 70, the bias term is a first linear function of EGV; for EAVs above 70 and below 180, the bias term is X or the bias term is a constant or a second linear function of EGV; and for EAVs above 180 and below 400, the bias term is an exponential function of EGV.

54. The method according to any of claims 33-53, wherein the analyte sensor is a glucose sensor, and wherein the analyte concentration is a glucose concentration.

55. An analyte sensor system, comprising: an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host; and Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 sensor electronics configured to perform operations comprising: generating estimated analyte values from the raw sensor signal; determining a rate-of-change for the estimated analyte values; determining a working electrode temperature; determining an elapsed time since sensor insertion; and determining a prediction horizon as a function of at least one of the elapsed time since insertion and the working electrode temperature and determining a time-lag-compensated estimated analyte value for one of the estimated analyte values as a function of the determined prediction horizon and the rate-of-change.

56. The analyte sensor system of claim 55, further comprising the analyte sensor system of any one of claims 2 to 26.

57. An analyte sensor system, comprising: an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host; and sensor electronics configured to perform operations comprising: generating estimated analyte values from the raw sensor signal; determining a rate-of-change for the estimated analyte values or the raw signal; determining a working electrode temperature; determining an elapsed time since sensor insertion; and adjusting estimated analyte values by applying a correction that is a function of the estimated analyte values.

58. The analyte sensor system of claim 57, further comprising the analyte sensor system of any one of claims 2 to 26.

59. A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, result in operations comprising: generating estimated analyte values of an analyte concentration of a host based at least on a raw sensor signal generated by an analyte sensor; Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 determining a rate-of-change for the estimated analyte values or the raw signal based at least on the raw sensor signal; dynamically determining a prediction horizon as a function of at least one of an elapsed time since sensor insertion and a working electrode temperature of a working electrode of the analyte sensor; and generating a time-lag-compensated estimated analyte value for at least one of the estimated analyte values as a function of at least the determined prediction horizon and the rate-of-change, thereby improving performance of the analyte sensor; or adjusting the estimated analyte values by applying a correction that is a function of at least the estimated analyte values, thereby improving performance of the analyte sensor.

60. The non-transitory computer readable medium storing instructions of claim 59, further comprising the operations of any one of claims 1 to 26.

61. An apparatus comprising: means for generating estimated analyte values of an analyte concentration of a host based at least on a raw sensor signal generated by an analyte sensor; means for determining a rate-of-change for the estimated analyte values or the raw signal based at least on the raw sensor signal; means for dynamically determining a prediction horizon as a function of at least one of an elapsed time since sensor insertion and a working electrode temperature of a working electrode of the analyte sensor; and means for generating a time-lag-compensated estimated analyte value for at least one of the estimated analyte values as a function of at least the determined prediction horizon and the rate-of-change, thereby improving performance of the analyte sensor; or means for adjusting the estimated analyte values by applying a correction that is a function of at least the estimated analyte values, thereby improving performance of the analyte sensor. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

62. The apparatus of claim 61, comprising: means for performing the operations of any one of claims 1 to 26.

63. An analyte sensor system, comprising: an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host; and sensor electronics configured to perform operations comprising: generating estimated analyte values from the raw sensor signal; maintaining historical data over a predetermined time period; applying a trained neural network model that receives as inputs current sensor parameters and the historical data to generate corrected analyte values; and outputting the corrected analyte values for use in analyte monitoring.

64. The analyte system according to claim 63, wherein the historical data includes a moving average of the estimated analyte values.

65. The analyte system according to claim 64, wherein the predetermined time period for the moving average of the estimated analyte values is about 50 minutes.

66. The analyte system according to any of claims 63-65, wherein the historical data includes a moving average of a rate of change of the estimated analyte values.

67. The analyte system according to claim 66, wherein the predetermined time period for the moving average of the rate of change of the estimated analyte values is about 50 minutes.

68. The analyte system according to any of claims 63-65, wherein the historical data includes point-wise historical data for the estimated analyte values. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

69. The analyte system according to claim 68, wherein the point-wise historical data includes equally spaced samples for the estimated analyte values.

70. The analyte system according to claim 69, wherein the equally spaced samples include five samples over an immediately preceding five hours.

71. The analyte system according to any of claims 68-70, wherein the point- wise historical data for the estimated analyte values includes samples of a filtered signal divided by a sensitivity value.

72. The analyte system according to any of claims 63-67, wherein the historical data includes a moving average of a filtered signal divided by a sensitivity value.

73. The analyte system according to claim 72, wherein the predetermined time period for the moving average of the filtered signal /sensitivity is about 50 minutes.

74. The analyte system according to any of claims 63-73, wherein the historical data includes a moving average of a measure of noise.

75. The analyte system according to claim 74, wherein the predetermined time period for the moving average of the measure of noise of the estimated analyte values is about 50 minutes.

76. The analyte system according to any of claims 63-75, wherein the inputs to the trained neural network further include an elapsed time since sensor insertion, a rate of change of the estimated analyte values, and a legacy estimated analyte value, the legacy estimated analyte value being determined using another algorithm.

77. The analyte system according to any of claims 63-76, wherein an output of the neural network provides a correction factor applied to an estimated analyte value algorithm. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

78. The analyte system according to claim 77, wherein the estimated analyte value algorithm includes inputs indicative of a progressive sensitivity decline, a baseline, and a dip and recovery for the estimated analyte values after implant.

79. The analyte system according to claim 77, wherein the estimated analyte value algorithm is based on the correction factor, an estimated sensitivity value, a prediction horizon value, a rate of change value, a filtered signal value, a progressive sensitivity decline value, a dip and recovery value, and a baseline.

80. The analyte system according to any of claims 63-79, further comprising implementing a soft capping mechanism using a hyperbolic tangent function to limit a magnitude of corrections applied by the neural network, and outputting final analyte values based on the soft-capped corrections

81. The analyte system according to claim 80, wherein implanting the soft capping mechanism includes implementing adaptive capping by: calculating a dynamic correction cap based on a moving average of historical correction magnitudes over a time window; applying a soft capping function to limit a correction delta using the dynamic correction cap; and generating final corrected analyte values by applying the soft-capped correction delta to the estimated analyte values

82. A method for enhancing analyte sensor accuracy, comprising: generating estimated analyte values from a raw sensor signal; maintaining historical data over a predetermined time period; applying a trained neural network model that receives as inputs current sensor parameters and the historical data to generate corrected analyte values; and outputting the corrected analyte values for use in analyte monitoring.

83. The method according to claim 82, wherein the historical data includes a moving average of the estimated analyte values. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

84. The method according to claim 83, wherein the predetermined time period for the moving average of the estimated analyte values is about 50 minutes.

85. The method according to any of claims 82-84, wherein the historical data includes a moving average of a rate of change of the estimated analyte values.

86. The method according to claim 85, wherein the predetermined time period for the moving average of the rate of change of the estimated analyte values is about 50 minutes.

87. The method according to any of claims 82-86, wherein the historical data includes point-wise historical data for the estimated analyte values.

88. The method according to claim 87, wherein the point-wise historical data includes equally spaced samples for the estimated analyte values.

89. The method according to claim 88, wherein the equally spaced samples include five samples over an immediately preceding five hours.

90. The method according to any of claims 87-89, wherein the point-wise historical data for the estimated analyte values includes samples of a filtered signal divided by a sensitivity value.

91. The method according to any of claims 82-86, wherein the historical data includes a moving average of a filtered signal divided by a sensitivity value.

92. The method according to claim 91, wherein the predetermined time period for the moving average of the filtered signal /sensitivity is about 50 minutes.

93. The method according to any of claims 82-92, wherein the historical data includes a moving average of a measure of noise. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

94. The method according to claim 93, wherein the predetermined time period for the moving average of the measure of noise of the estimated analyte values is about 50 minutes.

95. The method according to any of claims 82-94, wherein the inputs to the trained neural network further include an elapsed time since sensor insertion, a rate of change of the estimated analyte values, and a legacy estimated analyte value, the legacy estimated analyte value being determined using another algorithm.

96. The method according to any of claims 82-95, wherein an output of the neural network provides a correction factor applied to an estimated analyte value algorithm.

97. The method according to claim 96, wherein the estimated analyte value algorithm includes inputs indicative of a progressive sensitivity decline, a baseline, and a dip and recovery for the estimated analyte values after implant.

98. The method according to claim 96, wherein the estimated analyte value algorithm is based on the correction factor, an estimated sensitivity value, a prediction horizon value, a rate of change value, a filtered signal value, a progressive sensitivity decline value, a dip and recovery value, and a baseline.

99. The method according to any of claims 82-98, further comprising implementing a soft capping mechanism using a hyperbolic tangent function to limit a magnitude of corrections applied by the neural network, and outputting final analyte values based on the soft-capped corrections

100. The method according to claim 99, wherein implanting the soft capping mechanism includes implementing adaptive capping by: calculating a dynamic correction cap based on a moving average of historical correction magnitudes over a time window; applying a soft capping function to limit a correction delta using the Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 dynamic correction cap; and generating final corrected analyte values by applying the soft-capped correction delta to the estimated analyte values

101. The method of claim 82-100, wherein the historical data includes moving averages of estimated analyte values, rate-of-change measurements, noise measurements, and residual values over a historical time period, wherein each of the residual values refers to difference between a filtered and nonfiltered signal determined at an output of a Kalman filter.

102. An analyte sensor system, comprising: an analyte sensor configured to generate a raw sensor signal associated with an analyte concentration of a host; and sensor electronics configured to perform operations comprising: generating estimated analyte values from the raw sensor signal; applying a neural network correction to the estimated analyte values to generate corrected analyte values; implementing a soft capping mechanism to limit the magnitude of corrections applied by the neural network; and outputting adjusted analyte values based on the soft- capped corrections.

103. The analyte system according to claim 102, wherein the soft capping mechanism is implemented by applying a hyperbolic tangent function.

104. The analyte system according to any of claims 102-103, wherein the soft capping mechanism is implemented by implementing adaptive capping.

105. The analyte system according to claim 104, wherein implementing adaptive capping includes: calculating a dynamic correction cap based on a moving average of historical correction magnitudes over a time window; applying a soft capping function to limit a correction delta using the Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 dynamic correction cap; and generating final corrected analyte values by applying the soft-capped correction delta to the estimated analyte values.

106. The analyte system according to any of claims 102-105, wherein the magnitude of corrections is limited to a maximum of about 25 mg/dl.

107. The analyte system according to any of claims 102-106, wherein the soft capping mechanism limits less than 0.05% of the corrections.

108. A method for enhancing analyte sensor accuracy, comprising: generating a raw sensor signal associated with an analyte concentration of a host; generating estimated analyte values from the raw sensor signal; applying a correction, determined by a neural network, to the estimated analyte values to generate corrected analyte values; implementing a soft capping mechanism to limit the magnitude of corrections applied by the neural network; and outputting adjusted analyte values based on the soft-capped corrections.

109. The method according to claim 108, wherein implementing the soft capping mechanism includes applying a hyperbolic tangent function.

110. The method according to any of claims 108-109, wherein implanting the soft capping mechanism includes implementing adaptive capping.

111. The method according to claim 110, wherein implementing adaptive capping includes: calculating a dynamic correction cap based on a moving average of historical correction magnitudes over a time window; applying a soft capping function to limit the correction delta using the dynamic correction cap; and generating final corrected analyte values by applying the soft-capped correction delta to the estimated analyte values. Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01

112. The method according to any of claims 108-111, wherein the magnitude of corrections is limited to a maximum of about 25 mg/dl.

113. The method according to any of claims 108-112, wherein the soft capping mechanism limits less than 0.05% of the corrections.

114. A non-transitory computer readable medium storing instructions, which, when executed by at least one data processor, result in operations comprising: generating estimated analyte values from a raw sensor signal; maintaining historical data over a predetermined time period; applying a trained neural network model that receives as inputs current sensor parameters and the historical data to generate corrected analyte values; and outputting the corrected analyte values for use in analyte monitoring.

115. The non-transitory computer readable medium storing instructions of claim 114, further comprising the operations of any one of claims 83-101.

116. An apparatus, comprising: means for generating estimated analyte values from a raw sensor signal; means for maintaining historical data over a predetermined time period; means for applying a trained neural network model that receives as inputs current sensor parameters and the historical data to generate corrected analyte values; and means for outputting the corrected analyte values for use in analyte monitoring.

117. The apparatus of claim 116, comprising: means for performing the operations of any one of claims 83-101.

118. A non-transitory computer readable medium storing instructions, which, when executed by at least one data processor, result in operations comprising: Atty. Dkt. No.4855.144WO1 Client Reference No.0954-PCT01 generating a raw sensor signal associated with an analyte concentration of a host; generating estimated analyte values from the raw sensor signal; applying a correction, determined by the neural network, to the estimated analyte values to generate corrected analyte values; implementing a soft capping mechanism to limit a magnitude of corrections applied by the neural network; and outputting adjusted analyte values based on the soft-capped corrections.

119. The non-transitory computer readable medium storing instructions of claim 59, further comprising the operations of any one of claims 109-113.

120. An apparatus, comprising: means for generating a raw sensor signal associated with an analyte concentration of a host; means for generating estimated analyte values from the raw sensor signal; means for applying a correction, determined by a neural network, to the estimated analyte values to generate corrected analyte values; means for implementing a soft capping mechanism to limit a magnitude of corrections applied by the neural network; and means for outputting adjusted analyte values based on the soft-capped corrections.

121. The apparatus of claim 120, comprising: means for performing the operations of any one of claims 109-113.