CN114999608B - PICC home maintenance assessment method, system, storage medium and electronic device - Google Patents

PICC home maintenance assessment method, system, storage medium and electronic device Download PDF

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CN114999608B
CN114999608B CN202210566601.3A CN202210566601A CN114999608B CN 114999608 B CN114999608 B CN 114999608B CN 202210566601 A CN202210566601 A CN 202210566601A CN 114999608 B CN114999608 B CN 114999608B
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maintenance
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data set
catheter
acquiring
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CN114999608A (en
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赵路
尹茜
高凤莉
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Beijing Chaoyang Hospital
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Abstract

S1, collecting catheter position data of a three-dimensional coordinate system in a preset time period, acquiring a catheter position data set and a reference position data set, and acquiring a first difference value set; S2, acquiring a standard position data set, acquiring a second difference value set, comparing the magnitude relation between the first difference value and the second difference value within a preset time period to acquire a third difference value set, S3, acquiring a long-short-period memory network method model, S4, predicting future numerical values of the third difference value, S5, dividing a training set into a first maintenance requirement classification data set and a second maintenance requirement classification data set according to the predicted result and the correlation degree of the catheter maintenance requirement, S6, acquiring a plurality of fine classification data sets, and S7, acquiring a training model. The PICC home maintenance evaluation method provided by the embodiment of the invention has the effect of being beneficial to the maintenance of the catheter.

Description

PICC (peripherally inserted Central catheter) home maintenance evaluation method, PICC home maintenance evaluation system, storage medium and electronic equipment
Technical Field
The invention belongs to the technical fields of software engineering, big data, distributed storage, calculation and the like, and particularly relates to a PICC (peripherally inserted central catheter) home maintenance evaluation method, a PICC home maintenance evaluation system, a computer storage medium and electronic equipment.
Background
Placement of a central venous catheter (PICC) via a peripheral vein is a central venous access with a low incidence of complications, and may provide long-term chemotherapy venous access to tumor patients. Patients need to be discharged from a hospital with a tube in the intermittent period of receiving the chemotherapy, and the maintenance mode is mainly based on the maintenance of returning to the hospital at present. Discharge with PICC is a routine procedure for patients with long-term chemotherapy of tumors, PICC is maintained every 7 days without use, and catheter can be maintained for up to one year. When the patient is at home, the patient needs to subscribe to register and then the patient is in the hospital for maintenance and dressing change at the PICC maintenance clinic of the near three-level hospital.
Surveys show reduced compliance with line maintenance after patient discharge. The method has the advantages that the method is convenient to use, and has the advantages of being convenient to use, and capable of solving the problems of clinical treatment, including (1) the fact that the complication evaluation of the PICC after the patient is discharged from a hospital loses professional management and timely discovery and treatment, (2) the patient can be subjected to professional treatment when returning to the PICC catheter maintained in the hospital, (3) due to the fact that the patient evaluates the complications of the catheter and prevents insufficient cognition, a plurality of PICC catheter complications are delayed due to cognition deviation, and (3) the catheters with complications are required to be evaluated every day, and due to the fact that the maintenance of the catheter at home of the patient has a gear breaking problem.
Disclosure of Invention
In view of the above, the present invention provides a PICC home maintenance evaluation method, system, computer storage medium and apparatus, which can enable a patient to perform PICC home maintenance conveniently, and solve at least one problem in the background art.
In order to solve the technical problems, on the one hand, the invention provides a PICC home maintenance evaluation method, which comprises the following steps of S1, collecting catheter position data of a three-dimensional coordinate system in a preset time period, obtaining a catheter position data set, collecting reference position data in the three-dimensional coordinate system in the preset time period, obtaining a reference position data set, comparing the size between each data in the catheter position data set in the preset time period and corresponding reference position data to obtain a first difference value, obtaining a first difference value set, S2, collecting catheter standard positions of the three-dimensional coordinate system in the preset time period, obtaining a standard position data set, comparing the size between each standard position data and the corresponding reference position data in the preset time period to obtain a second difference value set, obtaining a third difference value set, comparing the size relationship between the first difference value and the second difference value in the preset time period, obtaining a third difference value set, obtaining a first difference value set, S3, storing a network demand for the three-dimensional coordinate system in the preset time period, obtaining a test model, storing the first difference value set as a training set, and a network demand for the test, storing the network demand for the test, and the network demand for the test is classified as a network, S3, storing the test result, and the network demand for the test is obtained by a network demand test, and the network demand is obtained by a method, and the network demand is classified as a test set, and the network demand is obtained by the test set, and the test set is obtained by the method and the test method. The method comprises the steps of obtaining a first maintenance requirement classification data set, obtaining a second maintenance requirement classification data set, wherein one of the first maintenance requirement classification data set and the second maintenance requirement classification data set is a maintenance-free classification data set, the other of the first maintenance requirement classification data set and the second maintenance requirement classification data set is a to-be-maintained classification data set, classifying the first maintenance requirement classification data set and the second maintenance requirement classification data set according to identity information, geographic information, motion information, time information and cannula duration information respectively to obtain a plurality of fine classification data sets, and placing the plurality of fine classification data sets of the first maintenance requirement classification data set and the second maintenance requirement classification data set in a network for training respectively to obtain a training model.
According to the PICC home maintenance evaluation method provided by the embodiment of the application, after a patient is intubated, information can be automatically uploaded and whether the catheter needs to be maintained in a future period of time can be judged in real time, so that the patient does not need to be in a state of worrying about catheter maintenance for a long time, anxiety emotion of the patient is relieved, and the catheter is timely maintained. In addition, the PICC home maintenance evaluation method provided by the embodiment of the application can also be used for carrying out targeted judgment according to different scenes, such as the scenes of rest, movement, lying and the like of a patient, so that the prediction accuracy is improved.
According to one embodiment of the invention, step S5 comprises dividing the training set into the classification data sets to be maintained if the predicted result is greater than a first threshold value, and dividing the training set into the classification data sets without maintenance if the predicted result is less than or equal to the first threshold value.
According to one embodiment of the invention, the step S7 comprises the steps of setting a fictitious patient module, wherein random identity information, geographic information, motion information, time information and intubation duration information are built in the fictitious patient module, acquiring the identity information, the geographic information, the motion information, the time information and the intubation duration information of the fictitious patient module, matching the fictitious patient module with a fine classification data set of one of the first maintenance requirement classification data set and the second maintenance requirement classification data set according to the acquired information at a first time and a second time respectively, matching the fictitious patient module with the fine classification data set of the other one of the first maintenance requirement classification data set and the second maintenance requirement classification data set according to the acquired information at the second time when matching results of the first time and the second time are inconsistent, ending training according to the acquired information at the second time to acquire the training model, and ending training when matching results of the first time and the second time are consistent.
According to one embodiment of the invention, the virtual patient module is internally provided with reply information aiming at a matching result, the virtual patient module can receive and send interactive information, training is finished when the virtual patient module replies with the matching result information or does not reply with the confirmation information, and the training model is associated with a third party when the virtual patient module replies with the non-matching result information, so that the training model is obtained.
5 According to one embodiment of the present invention, if the predicted result is greater than the first threshold and the classification data set to be maintained is classified into a self-maintenance classification data set and a professional maintenance classification data set according to the difference between the predicted result and the first threshold, wherein the classification data to be maintained is the self-maintenance classification data set when the difference between the predicted result and the first threshold is within a first range, and the classification data to be maintained is the professional maintenance classification data set when the difference between the predicted result and the first threshold is within a second range, and the first range is smaller than the second range.
According to one embodiment of the invention, the step S5 comprises the steps of obtaining user identifications of all vertexes in a training set, obtaining numerical value ranges of the first range and the second range, comparing the user identifications with the numerical value ranges of the first range and the second range, dividing the user identifications into a classification data set to be maintained if the user identifications are located in the numerical value range of the first range, and dividing the user identifications into the classification data set without maintenance if the user identifications are located in the numerical value range of the second range.
According to one embodiment of the invention, the reference position data is obtained by a wearable device comprising at least one of a watch, a bracelet, a necklace, glasses, etc.
According to one embodiment of the invention, the identity information comprises gender and age group, the geographic information comprises region, the movement information comprises movement time and movement type, the time information comprises date and real-time moment, and the cannula duration information comprises cannula duration, last maintenance time and predicted next maintenance time.
In a second aspect, the embodiment of the invention provides a PICC home maintenance evaluation system, which comprises a data monitoring module and a cloud platform module, wherein the data monitoring module is used for collecting catheter position data and reference position data, and the cloud platform module is used for receiving data information obtained by the data monitoring module, analyzing and processing the catheter position data, the reference position data and catheter standard position data to obtain catheter position information in future time and obtaining a decision result of whether maintenance of a catheter is needed.
In a third aspect, embodiments of the present invention provide a computer storage medium comprising one or more computer instructions which, when executed, implement a method as described in any of the preceding claims.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including a memory for storing one or more computer instructions and a processor for invoking and executing the one or more computer instructions to implement a method as described in any preceding claim.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of a PICC home maintenance assessment method according to an embodiment of the present invention;
Fig. 2 is a schematic diagram of an electronic device according to an embodiment of the invention.
Reference numerals:
An electronic device 300;
memory 310, operating system 311, application programs 312;
processor 320, network interface 330, input device 340, hard disk 350, and display device 360.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, features defining "first", "second" may include one or more such features, either explicitly or implicitly. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present invention, unless explicitly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected via an intervening medium, or in communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The PICC home maintenance assessment method according to the embodiment of the invention is described in detail below with reference to the accompanying drawings.
The PICC home maintenance evaluation method according to the embodiment of the application comprises the following steps:
S1, acquiring catheter position data of a three-dimensional coordinate system within a preset time period, and acquiring a catheter position data set. The method comprises the steps of collecting reference position data in a three-dimensional coordinate system within a preset time period, and obtaining a reference position data set. Comparing the magnitude between each data in the catheter position data set and the corresponding reference position data in a preset time period to obtain a first difference value, and obtaining a first difference value set.
Wherein the three-dimensional coordinate system may be a three-dimensional coordinate system composed of an X-axis, a Y-axis, and a Z-axis. When the predetermined time period is collected, the predetermined time period may be a set time period, for example, 0:00-5:00 in the early morning, 5:00-8:00 in the early morning, 8:00-12:00 in the morning, 12:00-13:00 in the midday, 13:00-17:30 in the afternoon, 17:30-18:00 in the evening, and 18:00-0:00 in the night, wherein the predetermined time period includes but is not limited to the embodiment of the time period, and the predetermined time period may be freely defined according to requirements.
The following description will take a period of 0:00-1:00 as an example.
During this time period, the person is typically in a deep sleep mode or occasionally getting up or not sleeping. If the person is in a deep sleep mode, respectively acquiring the positions of the catheter in the three-dimensional coordinate system at a plurality of moments within the time period to obtain a catheter position data set, and acquiring data of the reference position to obtain a reference position data set. For example, at a certain time to, the position of the catheter is (x 1,y1,z1), at the time the reference position is (x 01,y01,z01), and at the time the magnitude relation between the position of the catheter and the reference position, i.e., the first difference is (x 1-x01,y1-y01,z1-z01). At the next time t1, the position of the catheter is (x 2,y2,z2), at which time the reference position is (x 02,y02,z02), and at which time the magnitude relationship between the position of the catheter and the reference position, i.e., the first difference, is (x 2-x01,y1-y01,z1-z01). Likewise, if the person is in the exercise mode, the first difference may also be obtained through the above steps, which will not be described herein. It should be noted that the three-dimensional coordinate system may be used for positioning and measuring coordinates by using an existing GPS. The motion state and rest state of the patient can be judged by adopting the existing characteristics such as acceleration signals and the like, and the description is omitted here. Catheter position refers to the position of a point or center of gravity position of a site or location of a catheter in a three-dimensional coordinate system, and may include a placement position as well as a depth position. The reference position refers to a position of a reference point, and the reference position may be a position of an actual article, for example, a position of a matched wearable device, or may be a virtual set position. The catheter position and the reference position share a three-dimensional coordinate system for facilitating the acquisition of the first difference. In addition, a catheter position acquisition module may be mounted on the catheter-related device while acquiring the catheter position. In acquiring the reference position, a reference position acquisition module may be mounted on the catheter-related instrument separately from the catheter position acquisition module, or on a wearable device separately from the catheter-related instrument, for example, the reference position acquisition module may be designed on an elastic bandage that constrains the catheter.
S2, acquiring standard positions of the catheter in the three-dimensional coordinate system within a preset time period, acquiring a standard position data set, comparing the sizes of each standard position data and the corresponding reference position data within the preset time period to obtain a second difference value, acquiring a second difference value set, and comparing the size relation between the first difference value and the second difference value within the preset time period to obtain a third difference value, and acquiring the third difference value set. It should be noted that, the standard catheter positions at two adjacent moments may be different, for example, when the arm is extended downward, the standard catheter position is a first position, and when the arm is raised upward, the standard catheter position is a second position, that is, in the same coordinate system, the first position may be different from the second position. For conditions of different people, different postures and the like, the first positions at the same moment can be different by comparing the conditions with the same coordinate system. Namely, the position, the reference position and the standard position of the catheter can be changed in real time, and in order to improve accuracy, the first difference value and the second difference value are compared to obtain a third difference value.
For example, at a time to, the actual position of the catheter is (x 1,y1,z1), the reference position is (x 01,y01,z01), the standard position of the catheter is (x 01',y01',z01'), the second difference is (x 01'-x01,y01'-y01,z01'-z01), the first difference is (x 1-x01,y1-y01,z1-z01), and the third difference is ([(x01'-x01)-(x1-x01)],[(y01'-y01)-(y1-y0)],[(z01'-z01)-(z1-z01)]).
S3, processing the third difference value in the preset time period to obtain a data network set, taking the data network set as a sample set, dividing the sample set into a training set and a testing set, and testing the long-period memory network method through the testing set to obtain a long-period memory network method model. That is, the server can process the obtained third difference value set to obtain a data network set. The long-short term memory network method may be referred to as the LSTM method, which is applicable to the time series domain.
S4, acquiring real-time data of the training set, inputting the real-time data into a long-short-period memory network method model, and predicting a future value of the third difference value. That is, a third difference in future time may be predicted.
S5, dividing the training set into a first maintenance requirement classification data set and a second maintenance requirement classification data set according to the predicted result and the correlation degree of the catheter maintenance requirement, wherein one of the first maintenance requirement classification data set and the second maintenance requirement classification data set is a maintenance-free classification data set, and the other of the first maintenance requirement classification data set and the second maintenance requirement classification data set is a classification data set to be maintained. For example, when the first maintenance requirement classification data set is regarded as a maintenance-free classification data set, the second maintenance requirement classification data set is regarded as a classification data set to be maintained. For example, when the deviation of the third difference occurs less in a period of time in the future of prediction, the data of the corresponding training set may be divided into maintenance-free classification data sets, i.e., the catheter is not maintenance-free. If the deviation of the third difference value is predicted to be larger, the data of the corresponding training set can be divided into a classification data set to be maintained, namely, the catheter needs to be maintained.
It should be noted that, since different information related to the patient affects the variation of the third difference value in the future time, for example, in the case of a child who moves more than one time, a patient who moves for a certain time, a patient who is in a non-resting state in the daytime, has excessive clothing passing in a cold environment, etc., the third difference value may vary greatly in the future time. And the third difference value will change less in a future period of time when the clothing is worn less in a rest state, a relaxed movement pattern and a hot environment. Therefore, in this embodiment, when the correlation between the predicted result and the catheter maintenance requirement is determined, the determination can be performed by the change value generated by the third difference value, so that the method is applicable to different user information features, and the method does not need to set a determination method for different features, so that the method has the advantages of capability of processing data in batches, strong universality and the like.
S6, classifying the first maintenance requirement classification data set and the second maintenance requirement classification data set according to the identity information, the geographic information, the motion information, the time information and the cannula duration information respectively to obtain a plurality of fine classification data sets. For example, the server may further classify the obtained first and second maintenance requirement classification data sets according to different limited information, such as identity information, geographical information, movement information, time information, and cannula duration information of the patient, respectively. The identity information can comprise sex, age, disease history information, near-relative disease history information, information of the same disease of the region where the user is located and the like of the patient. The geographic information may include ambient geographic location information, regional natural environment information, etc. associated with the patient. The athletic information may include the type of athletic activity being undertaken by the patient, the type of athletic activity already undertaken, the duration of athletic activity likely to be undertaken in the future, etc. The cannula length information may include the length of the cannula, historical cannula information, and the like. It should be noted that, from the beginning of the placement, the catheter is recorded in its brand, model, placement site, depth, exposure, circumference, catheter tip positioning chest, and information from each maintenance use thereafter, and can be used as a basis for classification of the fine classification dataset.
And S7, placing a plurality of fine classification data sets of the first maintenance requirement classification data set and the second maintenance requirement classification data set in a network for training respectively to obtain a training model. When the cannula maintenance condition of a specific patient is specifically analyzed, the identity information, the geographic information, the motion information, the time information, the cannula duration information and the like of the patient can be input into the training model, and then the training model can obtain feedback information according to the input information to prompt whether the catheter needs maintenance in a future period of time.
It should be noted that, the identity information, the geographic information, the movement information, the time information, the cannula duration information and the like of the patient are associated with the maintenance information of the cannula, which is beneficial to personalized analysis of the causes of the complications. And the rehabilitation exercise mode of different patients can be obtained according to the association between the exercise information and the maintenance information of the cannula, for example, when the exercise exceeding amount or the exercise amplitude is monitored, the maintenance probability of the catheter can be increased, and a warning prompt can be sent to the user.
Therefore, according to the PICC home maintenance evaluation method provided by the embodiment of the application, after a patient is intubated, information can be automatically uploaded and whether the catheter needs to be maintained in a future period of time can be judged in real time, so that the patient does not need to be in a state of worrying about catheter maintenance for a long time, anxiety emotion of the patient is relieved, and the catheter is timely maintained. In addition, the PICC home maintenance evaluation method provided by the embodiment of the application can also be used for carrying out targeted judgment according to different scenes, such as the scenes of rest, movement, lying and the like of a patient, so that the prediction accuracy is improved.
According to one embodiment of the application step S5 comprises the step of dividing the training set into a classification data set to be maintained if the predicted outcome is greater than a first threshold and dividing the training set into a classification data set without maintenance if the predicted outcome is less than or equal to the first threshold. That is, if the third difference is too large and is greater than the first threshold range, it indicates that the catheter needs to be serviced in the future for some time. Conversely, if the third difference deviation is not large, i.e., less than the first threshold range, it is indicated that the catheter will not need to be serviced in the future at some time. When the predicted result is equal to the first threshold, the catheter can be maintained or not, and whether the catheter needs to be maintained can be judged according to autonomous evaluation or external evaluation of the patient. Further, if the predicted result is greater than the first threshold, the interval duration of the next prediction is shortened, and the prediction accuracy and precision are further improved.
In some embodiments of the application, step S7 comprises the steps of:
Setting a virtual patient module, wherein random identity information, geographic information, motion information, time information and cannula duration information are arranged in the virtual patient module. For example, the patient is a 6 year old child, and the time period is 7 months, and is located in northeast China, and the length of the intubation period is 1 day.
Identity information, geographic information, movement information, time information, and cannula length information of the hypothetical patient module are obtained.
Matching the phantom patient module with the fine classification data set of one of the first maintenance need classification data set and the second maintenance need classification data set at a first time and a second time, respectively, based on the acquired information. For example, a hypothetical patient module is matched to a fine classification dataset that does not require maintenance of the classification dataset.
And when the matching results of the first time and the second time are inconsistent, matching the imaginary patient module with the fine classification data set of the other one of the first maintenance requirement classification data set and the second maintenance requirement classification data set according to the acquired information of the second time, and ending training to obtain a training model. For example, if the matching results of the previous step are inconsistent, the hypothetical patient module is matched to the fine classification dataset of the classification dataset to be maintained.
And ending training when the matching results of the first time and the second time are consistent, and obtaining a training model.
In this embodiment, training is performed by comparing the matching results of the first time and the second time, which has the advantage of saving training time.
According to one embodiment of the application, the hypothetical patient module has reply information for the matching result built in, and can receive and send interactive information, for example, the hypothetical patient module can receive advice sent by medical staff and can also consult with the medical staff.
The training is ended when the hypothetical patient module replies with either consent to match the result information or no confirmation information.
When the imaginary patient module replies the information of the disagreement matching result, the training model is associated with a third party to obtain the training model, for example, although the judgment that the catheter is not required to be maintained in a future period of time is carried out, the patient does not agree with the judgment, the patient can also consult with the third party, the third party can be a medical staff or a hospital, and the medical staff judges whether the catheter is required to be maintained or applies for reservation maintenance to the hospital.
When judging that the catheter needs to be maintained in a period of time in the future, the third party can actively send information to the patient module, the patient module can receive the information, communication with a patient can be enhanced through active service of medical staff, anxiety and fear psychology of the patient can be relieved, the patient is helped to set up the confidence of winning the disease, and the patient is prompted to actively conduct pipeline maintenance regularly.
In the event of complications of PICC, the patient may be asked to a third party by means of mutual information. The third party can also survey various recent information of the patient and evaluate the complications of the PICC after the patient is discharged, so that the complications of the PICC after the patient is discharged are evaluated and are professionally managed and timely found and processed.
In addition, the patient can evaluate by himself, and once abnormality occurs, the patient can take a picture and upload, and the expert preliminary evaluation gives advice. Has the advantages of strong reminding of re-diagnosis, improving the probability of finding complications at home, early finding, early re-diagnosis, early treatment and the like. For complications already occurring, tracking and observing can be performed by photographing the last function, and nursing is guided by professionals in a third party.
According to one embodiment of the application, if the predicted outcome is greater than the first threshold and based on the difference of the predicted outcome and the first threshold, the classification data set to be maintained is classified into a self-maintenance classification data set and a professional maintenance classification data set.
When the difference value between the predicted result and the first threshold value is in a first range, the classified data to be maintained is a self-maintenance classified data set, and when the difference value between the predicted result and the first threshold value is in a second range, the classified data to be maintained is a professional maintenance classified data set, and the first range is smaller than the second range. That is, when the predicted result is greater than the first threshold by a certain range, it is judged that the catheter can be maintained by itself or by an assistant, and the maintenance person here may be a person having less experience and expertise. When the predicted outcome is greater than the first threshold, a determination is made that maintenance is required for a professional, where the professional may be a healthcare professional, or a person with specific training and certification.
According to one embodiment of the application, step S5 comprises the steps of:
User identification of each vertex in the trained set is obtained, wherein the user identification can be identity information, geographic information and the like of a patient.
And acquiring the numerical ranges of the first range and the second range.
And comparing the user identifier with the numerical ranges of the first range and the second range, dividing the user identifier into a classification data set to be maintained if the user identifier is positioned in the numerical range of the first range, and dividing the user identifier into a classification data set without maintenance if the user identifier is positioned in the numerical range of the second range.
In the present embodiment, by comparing the user identification with the first range and the second range, it is advantageous to shorten the time required for classification of the fine classification data set, and the prediction accuracy can be improved.
In some embodiments of the application, the reference position data is obtained by a wearable device comprising at least one of a watch, a bracelet, a necklace, glasses, etc. It should be noted that, when the reference position data is acquired, the state and the mode of the user may be combined for judgment, for example, the specific judgment is performed according to different scenes, for example, the scenes of rest, movement, lying and the like of the patient are respectively judged, so as to improve the prediction accuracy.
According to one embodiment of the application, the identity information comprises gender and age group, the geographical information comprises region, the movement information comprises movement time and movement type, the time information comprises date and real-time moment, the cannula duration information comprises cannula duration, last maintenance time and predicted next maintenance time, and the accuracy of judging whether the catheter needs maintenance in a future period can be improved by refining the information of the patient.
The application also discloses a PICC home maintenance evaluation system which comprises a data monitoring module and a cloud platform module, wherein the data monitoring module is used for collecting the catheter position data and the reference position data, the cloud platform module is used for receiving the data information obtained by the data monitoring module, analyzing and processing the catheter position data, the reference position data and the catheter standard position data to obtain the catheter position information in future time and obtain a decision result of whether the catheter needs to be maintained or not. Through adopting cloud platform module, not only can be from putting into the beginning with the pipe, brand, model, put into position, degree of depth, expose, the arm is enclosed, the data that the pipe pointed end was used of keeping in position chest film and later each time is through system integration management to no matter what the hospital was maintained to the patient, all available cloud platform module inner catheter maintenance use data is come to know patient PICC and put into use and maintain the whole circumstances.
Meanwhile, the cloud platform module is further provided with a maintenance reminding function for reminding a patient of regular maintenance, standardizing the regular maintenance, prolonging the service life of the catheter, reducing the occurrence probability of complications, and simultaneously finding out the occurrence of warning complications as soon as possible. In addition, PICC is maintenance evaluation system at home can install and use as the software cooperation in electronic equipment, and the software can directly reserve and maintain the outpatient service, convenient and fast is equipped with healthy propaganda and education plate in the software simultaneously to push video or file is continuously announced and taught to the patient. The accumulated learning duration of the video of the study is watched by the patient in a clicking way, the accumulated point exchange gift can be obtained, and the self-management capability of the patient can be improved by adopting software. The method has the advantages that a patient enters a personal homepage and can select corresponding functions according to requirements, basic information in software covers all information of the catheter, including X-ray positioning imaging data after the catheter is successfully placed, a patient can see a past catheter placement maintenance record no matter where the patient is in a hospital or is in maintenance, and PICC maintenance outpatient service is reserved directly through the software.
That is, the system may include a data monitoring module and a cloud platform module. The catheter position and the reference position can be acquired through the data monitoring module, for example, the data monitoring module comprises a wireless sensor, a GPS (global positioning system) positioner and the like, and future data information can be judged and predicted through the cloud platform module to obtain a conclusion whether the catheter needs maintenance or not. The cloud platform module can comprise a data storage and processing module, the data storage and processing module can process and analyze the acquired data information and the like to obtain a decision result, and the decision result is sent to an information receiving module which can acquire the decision result by a user.
In summary, according to the PICC home maintenance evaluation method and the PICC home maintenance evaluation system provided by the embodiment of the application, the PICC can be controlled in the whole process, the PICC management maintenance is realized, the difficult and complicated complications are monitored, and the work guidance can be provided for medical staff.
In addition, the embodiment of the invention also provides a computer storage medium, which comprises one or more computer instructions, wherein the one or more computer instructions realize the PICC home maintenance evaluation method when being executed.
That is, the computer storage medium stores a computer program which, when executed by a processor, causes the processor to perform any one of the PICC home maintenance evaluation methods described above.
As shown in fig. 2, an embodiment of the present invention provides an electronic device 300, including a memory 310 and a processor 320, where the memory 310 is configured to store one or more computer instructions, and the processor 320 is configured to invoke and execute the one or more computer instructions, thereby implementing any of the methods described above.
That is, the electronic device 300 comprises a processor 320 and a memory 310, in which memory 310 computer program instructions are stored, wherein the computer program instructions, when executed by the processor, cause the processor 320 to perform any of the methods described above.
Further, as shown in fig. 2, the electronic device 300 also includes a network interface 330, an input device 340, a hard disk 350, and a display device 360.
The interfaces and devices described above may be interconnected by a bus architecture. The bus architecture may be a bus and bridge that may include any number of interconnects. One or more Central Processing Units (CPUs), represented in particular by processor 320, and various circuits of one or more memories, represented by memory 310, are connected together. The bus architecture may also connect various other circuits together, such as peripheral devices, voltage regulators, and power management circuits. It is understood that a bus architecture is used to enable connected communications between these components. The bus architecture includes, in addition to a data bus, a power bus, a control bus, and a status signal bus, all of which are well known in the art and therefore will not be described in detail herein.
The network interface 330 may be connected to a network (e.g., the internet, a local area network, etc.), and may obtain relevant data from the network and store the relevant data in the hard disk 350.
The input device 340 may receive various instructions from an operator and transmit the instructions to the processor 320 for execution. The input device 340 may include a keyboard or pointing device (e.g., a mouse, a trackball, a touch pad, or a touch screen, among others).
The display device 360 may display results obtained by the processor 320 executing instructions.
The memory 310 is used for storing programs and data necessary for the operation of the operating system, and data such as intermediate results in the calculation process of the processor 320.
It will be appreciated that memory 310 in embodiments of the invention may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM), erasable Programmable Read Only Memory (EPROM), electrically Erasable Programmable Read Only Memory (EEPROM), or flash memory, among others. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. The memory 310 of the apparatus and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some embodiments, memory 310 stores elements, executable modules or data structures, or a subset thereof, or an extended set thereof, operating system 311 and application programs 312.
The operating system 311 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application programs 312 include various application programs such as a Browser (Browser) and the like for implementing various application services. A program implementing the method of the embodiment of the present invention may be included in the application program 312.
The processor 320, when calling and executing the application program and the data stored in the memory 310, specifically, the program or the instruction stored in the application program 312, sends one of the first set and the second set to the node distributed by the other of the first set and the second set, wherein the other is stored in at least two nodes in a scattered manner, and performs intersection processing in a node-by-node manner according to the node distribution of the first set and the node distribution of the second set.
The method disclosed in the above embodiment of the present invention may be applied to the processor 320 or implemented by the processor 320. Processor 320 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 320. The processor 320 may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, or discrete hardware components, which may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 310 and the processor 320 reads the information in the memory 310 and in combination with its hardware performs the steps of the method described above.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
In particular, the processor 320 is further configured to read the computer program and execute any of the methods described above.
In the several embodiments provided in the present application, it should be understood that the disclosed methods and apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may be physically included separately, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform part of the steps of the transceiving method according to the embodiments of the present invention. The storage medium includes various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, or an optical disk.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.

Claims (7)

1. The PICC home maintenance evaluation method is characterized by comprising the following steps of:
S1, acquiring catheter position data of a three-dimensional coordinate system in a preset time period, acquiring a catheter position data set, acquiring reference position data in the three-dimensional coordinate system in the preset time period, acquiring a reference position data set, comparing the size between each data in the catheter position data set in the preset time period and the corresponding reference position data to obtain a first difference value, and acquiring a first difference value set;
S2, acquiring a catheter standard position of the three-dimensional coordinate system in the preset time period, acquiring a standard position data set, comparing the size between each piece of standard position data and the corresponding reference position data in the preset time period to acquire a second difference value, acquiring a second difference value set, comparing the size relation between the first difference value and the second difference value in the preset time period to acquire a third difference value, and acquiring a third difference value set;
S3, processing the third difference value in the preset time period to obtain a data network set, taking the data network set as a sample set, dividing the sample set into a training set and a testing set, and testing a long-period memory network method through the testing set to obtain a long-period memory network method model;
S4, acquiring real-time data of the training set, inputting the real-time data into the long-short-period memory network method model, and predicting a future value of the third difference value;
S5, dividing the training set into a first maintenance requirement classification data set and a second maintenance requirement classification data set according to the predicted result and the correlation degree of the catheter maintenance requirement;
One of the first maintenance requirement classification data set and the second maintenance requirement classification data set is a maintenance-free classification data set, and the other of the first maintenance requirement classification data set and the second maintenance requirement classification data set is a to-be-maintained classification data set;
Dividing the training set into the classified data sets to be maintained if the predicted result is greater than a first threshold value, and dividing the training set into the classified data sets without maintenance if the predicted result is less than or equal to the first threshold value;
S6, classifying the first maintenance requirement classification data set and the second maintenance requirement classification data set according to identity information, geographic information, motion information, time information and cannula duration information respectively to obtain a plurality of fine classification data sets;
s7, placing the first maintenance requirement classification data set and the plurality of fine classification data sets of the second maintenance requirement classification data set in a network for training respectively to obtain a training model;
Setting a fictitious patient module, wherein random identity information, geographic information, motion information, time information and cannula duration information are arranged in the fictitious patient module;
Acquiring identity information, geographic information, movement information, time information and cannula duration information of the imaginary patient module;
Matching the phantom patient module with a fine classification dataset of one of the first and second maintenance need classification datasets according to the acquired information at a first time and a second time, respectively;
When the matching results of the first time and the second time are inconsistent, matching the fictitious patient module with the fine classification data set of the other one of the first maintenance requirement classification data set and the second maintenance requirement classification data set according to the acquired information of the second time, and ending training to obtain the training model;
ending training when the matching results of the first time and the second time are consistent, and obtaining the training model;
The virtual patient module is internally provided with reply information aiming at a matching result, and can receive and send interactive information;
ending training when the hypothetical patient module replies to agree to the matching result information or does not reply to the confirmation information;
and when the imaginary patient module replies the information of the disagreement of the matching result, the training model is associated with a third party, and the training model is obtained.
2. The PICC home maintenance evaluation method of claim 1, wherein if the predicted outcome is greater than the first threshold and based on a difference of the predicted outcome and the first threshold, classifying the to-be-maintained classified dataset into a self-maintenance classified dataset and a professional maintenance classified dataset;
and when the difference value between the predicted result and the first threshold value is in a second range, the classification data to be maintained is the professional maintenance classification data set, and the first range is smaller than the second range.
3. The PICC home maintenance evaluation method of claim 2, wherein step S5 includes:
Obtaining user identifiers of all vertexes in the training set;
acquiring the numerical value ranges of the first range and the second range;
Comparing the user identifier with the numerical ranges of the first range and the second range, dividing the user identifier into a classified data set to be maintained if the user identifier is located in the numerical range of the first range, and dividing the user identifier into the classified data set without maintenance if the user identifier is located in the numerical range of the second range.
4. The PICC home maintenance assessment method of claim 1, wherein the identity information includes gender and age, the geographic information includes territories, the movement information includes movement time, movement type, the time information includes date and real-time, and the cannula length information includes a length of a cannula, a last maintenance time, and a predicted next maintenance time.
5. A PICC home maintenance assessment system for maintaining a PICC using the PICC home maintenance assessment method according to any one of claims 1 to 4, comprising:
The data monitoring module is used for collecting catheter position data and reference position data;
The cloud platform module is used for receiving the data information obtained by the data monitoring module, analyzing and processing the catheter position data, the reference position data and the catheter standard position data, obtaining the catheter position information in future time and obtaining a decision result of whether the catheter needs to be maintained or not.
6. A computer storage medium comprising one or more computer instructions which, when executed, implement the method of any of claims 1-4.
7. An electronic device comprising a memory and a processor, characterized in that,
The memory is used for storing one or more computer instructions;
the processor is configured to invoke and execute the one or more computer instructions to implement the method of any of claims 1-4.
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Inventor after: Yin Qian

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