CN112037932A - Patient medication behavior intervention method and device, server and storage medium - Google Patents
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Abstract
The application discloses a patient medication behavior intervention method and device, a server and a storage medium, which are applicable to medical science and technology, wherein the method comprises the following steps: acquiring behavior data of a target patient within a preset time length, wherein the behavior data comprises non-medication behavior data, the preset time length comprises n time periods, and n is a natural number; learning the behavior data through a long-time memory network LSTM to obtain a medication behavior probability sequence in the (n + 1) th time period; determining the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence; and determining a target intervention strategy according to the medication behavior type and the non-medication behavior data, and sending medication reminding information to the target patient based on the target intervention strategy. By adopting the application, the medication compliance of patients can be improved.
Description
Technical Field
The application relates to the technical field of medical health service internet, in particular to a patient medication behavior intervention method and device, a server and a storage medium.
Background
Poor medication compliance is a common problem in chronic disease management, and causes of the problem are mainly as follows: the patient does not know the medication compliance in place, and the medicine can be stopped when the condition of the patient is considered to be improved, and the medicine is stopped when the hypertension patient finds that the blood pressure is recovered to be normal; the degree of education of the clinician on the patient is insufficient, the patient does not master the related knowledge, and the situations of forgetting to take the medicine, wrong dosage and the like exist.
In the prior art, the medicine intervention aiming at the patient is usually carried out when the patient stops taking the medicine or does not take the medicine on time and the body is uncomfortable, and the intervention is carried out when the patient goes to the doctor again and is not timely. In addition, in order to manage a large number of patients, the prior art medication intervention usually only simply intervenes on the classification of drugs, and the intervention effect is poor and the applicability is poor.
Disclosure of Invention
The application provides a patient medication behavior intervention method and device, a server and a storage medium, which can improve the medication compliance of patients and have high applicability.
In a first aspect, the present application provides a method for intervening in a medication act of a patient, comprising:
acquiring behavior data of a target patient within a preset time length, wherein the behavior data comprises non-medication behavior data, the preset time length comprises n time periods, and n is a natural number;
learning the behavior data through a long-time memory network LSTM to obtain a medication behavior probability sequence of the (n + 1) th time period;
determining the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence;
and determining a target intervention strategy according to the medication behavior type and the non-medication behavior data, and sending medication reminding information to the target patient based on the target intervention strategy.
With reference to the first aspect, in one possible implementation manner, the learning the behavior data through a long and short term memory network LSTM includes:
sequencing the behavior data within the preset duration according to a preset sequence to obtain a sequenced behavior data sequence, and inputting the behavior data sequence into the LSTM;
and learning the medication behavior type of the next time period corresponding to the behavior data of each time period in the n time periods by using a multi-classification problem of a plurality of medication behavior types as a learning task through the LSTM.
With reference to the first aspect, in a possible implementation manner, one medication behavior probability in the medication behavior probability sequence corresponds to one medication behavior type;
the determining the medication behavior type of the target patient in the (n + 1) th time slot according to the medication behavior probability sequence includes:
and determining the medication behavior type corresponding to the maximum medication behavior probability in the medication behavior probability sequence as the medication behavior type of the target patient in the (n + 1) th time period.
With reference to the first aspect, in a possible implementation manner, the determining a target intervention policy according to the medication behavior type and the non-medication behavior data includes:
generating a self-management label of the target patient according to the medication behavior type and the non-medication behavior data;
calculating the matching degree of the self-management label and the strategy labels of each intervention strategy in an intervention strategy set to obtain a plurality of matching degree values, wherein the preset intervention strategy set comprises a plurality of intervention strategies, and one intervention strategy carries at least one strategy label;
and determining the intervention strategy corresponding to the maximum value in the plurality of matching values as the target intervention strategy.
With reference to the first aspect, in a possible implementation manner, the target intervention policy includes a medication reminding mode and a medication reminding frequency;
the sending of medication reminding information to the target patient based on the target intervention strategy includes:
sending the medication reminding information to the target patient in the medication reminding mode according to the medication reminding frequency in a preset time period;
the medication reminding mode comprises a text prompt or a voice prompt.
With reference to the first aspect, in a possible implementation manner, the acquiring behavior data of the target patient within a preset time period includes:
sending a behavior data acquisition prompt to a target patient according to a preset frequency to prompt the target patient to feed back behavior data according to a target data acquisition form, wherein the target data acquisition form comprises webpage link acquisition or two-dimensional code acquisition;
and receiving the behavior data fed back by the target patient as the behavior data of the target patient within a preset time length.
In a second aspect, the present application provides a patient medication behavior intervention device comprising:
the behavior data acquisition module is used for acquiring behavior data of a target patient within a preset time length, wherein the behavior data comprises non-medication behavior data, the preset time length comprises n time periods, and n is a natural number;
the probability sequence learning module is used for learning the behavior data through a long-time memory network LSTM so as to obtain a medication behavior probability sequence in the (n + 1) th time period;
a behavior type determining module, configured to determine a medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence;
and the determining and sending module is used for determining a target intervention strategy according to the medication behavior type and the non-medication behavior data and sending medication reminding information to the target patient based on the target intervention strategy.
With reference to the second aspect, in a possible implementation manner, the target intervention strategy includes a medication reminding mode and a medication reminding frequency;
the determination sending module is used for sending the medication reminding information to the target patient in the medication reminding mode according to the medication reminding frequency in a preset time period;
the medication reminding mode comprises a text prompt or a voice prompt.
With reference to the second aspect, in a possible implementation manner, the probability sequence learning module is configured to:
sequencing the behavior data within the preset duration according to a preset sequence to obtain a sequenced behavior data sequence, and inputting the behavior data sequence into the LSTM;
and learning the medication behavior type of the next time period corresponding to the behavior data of each time period in the n time periods by using a multi-classification problem of a plurality of medication behavior types as a learning task through the LSTM.
With reference to the second aspect, in a possible implementation manner, one medication behavior probability in the medication behavior probability sequence corresponds to one medication behavior type;
the behavior type determining module is configured to determine the medication behavior type corresponding to the maximum medication behavior probability in the medication behavior probability sequence as the medication behavior type of the target patient in the (n + 1) th time period.
With reference to the second aspect, in a possible implementation manner, the determining and sending module includes:
a label generating unit, configured to generate a self-management label of the target patient according to the medication behavior type and the non-medication behavior data;
the matching degree calculation unit is used for calculating the matching degree of the self-management tag and the strategy tags of each intervention strategy in the intervention strategy set to obtain a plurality of matching degree values, the preset intervention strategy set comprises a plurality of intervention strategies, and one intervention strategy carries at least one strategy tag;
and a policy determining unit, configured to determine, as the target intervention policy, an intervention policy corresponding to a maximum value of the multiple matching values.
With reference to the second aspect, in a possible implementation manner, the behavior data obtaining module includes:
the device comprises a sending prompting unit, a processing unit and a processing unit, wherein the sending prompting unit is used for sending behavior data acquisition prompts to target patients according to preset frequency so as to prompt the target patients to feed back behavior data according to a target data acquisition form, and the target data acquisition form comprises webpage link acquisition or two-dimension code acquisition;
and the data receiving unit is used for receiving the behavior data fed back by the target patient as the behavior data of the target patient within a preset time length.
In a third aspect, the present application provides a server, comprising a processor, a memory and a transceiver, wherein the processor, the memory and the transceiver are connected to each other, wherein the memory is used for storing a computer program supporting the text encryption device to execute the intervention method of the drug administration behavior of the patient, and the computer program comprises program instructions; the processor is configured to invoke the program instructions to perform the method for intervention in a medication behaviour of a patient as described in the first aspect of the present application.
In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, the computer program comprising program instructions; the program instructions, when executed by a processor, cause the processor to perform a method of intervention in a medication behaviour of a patient as described in the first aspect of the present application.
In the application, a medication behavior intervention platform acquires behavior data of a target patient within a preset time length, wherein the behavior data comprises non-medication behavior data, the preset time length comprises n time periods, and n is a natural number; learning the behavior data through a long-time memory network LSTM to obtain a medication behavior probability sequence in the (n + 1) th time period; determining the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence; and determining a target intervention strategy according to the medication behavior type and the non-medication behavior data, and sending medication reminding information to the target patient based on the target intervention strategy. By adopting the application, the medication compliance of patients can be improved.
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In order to more clearly illustrate the technical solutions of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic diagram of the architecture of a medication behavior intervention system provided herein;
FIG. 2 is a schematic flow chart of a method of intervention in a patient medication act provided herein;
FIG. 3 is another schematic flow chart of a method of intervention in a patient's medication behavior as provided herein;
FIG. 4 is a schematic structural diagram of a patient medication behavior intervention device provided herein;
fig. 5 is a schematic structural diagram of a server provided in the present application.
Detailed Description
The technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings in the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The application provides a patient medication behavior intervention method, which can learn the behavior data of the previous n time periods through a long-short-term memory (LSTM) network model according to the behavior data of the target patient in the previous n time periods to determine the medication behavior type of the target patient in the (n + 1) th time period, determine a target intervention strategy according to the medication behavior type and non-medication behavior data in the behavior data of the previous n time periods, and send medication reminding information to the target patient based on the target intervention strategy, so that the medication dependency of the patient is improved.
The patient medication behavior intervention method provided by the application can be applied to a medication behavior intervention system, the system comprises a medication behavior intervention platform and a patient terminal cluster, please refer to fig. 1, which is an architecture schematic diagram of the medication behavior intervention system provided by the application. As shown in fig. 1, the framework diagram includes a medication behavior intervention platform 100 and a patient terminal cluster 101, where the patient terminal cluster 101 may include a plurality of patient terminals, as shown in fig. 1, specifically, a patient terminal 101a, a patient terminal 101b, patient terminals 101c and …, and a patient terminal 101n, where a target patient terminal may be any one of the patient terminals in the patient terminal cluster 101, and the present application is described with the end patient 101a in fig. 1 as the target patient terminal.
Each patient terminal in the medication behavior intervention platform 100 and the patient terminal cluster 101 may be a computer device, including a mobile phone, a tablet computer, a notebook computer, a handheld computer, a mobile internet device (MID, mobile internet device), a Point Of Sale (POS) machine, a wearable device (e.g., a smart watch, a smart bracelet, etc.), and the like.
In the patient medication behavior intervention method provided by the application, the medication behavior intervention platform 100 may send a behavior data acquisition prompt to the patient terminal 101a according to a preset frequency to prompt a target patient of the patient terminal 101a to feed back the behavior data according to a target data acquisition form. Here, the preset frequency may be once a day, once a week, and the like, and the target data collection form may include web link collection or two-dimensional code collection, which is not limited in this application. The medication behavior intervention platform 100 receives the behavior data fed back by the patient terminal 101a as the behavior data of the target patient in the previous n time periods. Here, n is a natural number, and the behavior data may include medication behavior data, which may include medicine data, medicine taking data, and withdrawal data, etc., and non-medication behavior data, which may include patient basic data, lifestyle data, physical symptom data, etc. The medication behavior intervention platform 100 can learn the behavior data of the target patient in the previous n time periods through the LSTM, so as to obtain a medication behavior probability sequence of the n +1 time period, and determine the medication behavior type corresponding to the maximum value of the medication behavior probability in the medication behavior probability sequence as the medication behavior type of the target patient in the n +1 time period. Then, the medication behavior intervention platform 100 generates a self-management label according to the medication behavior type and the non-medication behavior data, calculates a matching value between the self-management label and a policy label of each intervention policy in a preset intervention policy set, determines a target intervention policy according to the matching value, and sends medication reminding information to the patient terminal 101a where the target patient is located according to the target intervention policy.
For convenience of description, the method for intervening in the medication behavior of the patient provided by the present application will be exemplified with reference to fig. 2 to 3 by taking the medication behavior intervening platform as an execution subject.
Referring to fig. 2, a flow chart of the intervention method for the medication behavior of the patient provided by the present application is shown. As shown in fig. 2, the method provided by the present application may include the following steps:
s101, acquiring behavior data of the target patient within a preset time length.
In some possible embodiments, the preset time period includes n time periods, where n is a natural number. For example, the preset duration may be one month and the period may be one week. The behavioral data may include medication behavioral data and non-medication behavioral data. The medication behavior data can include medicine data (such as amlodipine besylate tablet, 5 mg/time and 1 time/day), medicine taking data (such as forgetting to take medicine and forgetting to take medicine 1 time), medicine stopping data (such as intentionally stopping medicine), and the like. Non-medication behavior data may include patient profile data (e.g., age, gender, race, height, weight), lifestyle data (e.g., whether to smoke, whether to drink, etc.), and physical symptom data (e.g., blood pressure level changes, whether to have dizziness, whether to have blurred vision, etc.).
In some possible embodiments, the medication behavior intervention platform sends a behavior data acquisition prompt to the target patient according to a preset frequency to prompt the target patient to feed back the behavior data according to the target data acquisition form, and receives the behavior data fed back by the target patient as the behavior data of the target patient within a preset time length. The target data acquisition mode comprises webpage link acquisition or two-dimensional code acquisition.
For example, assuming that the preset time duration is two months from july to august, that is, eight weeks, and the preset frequency is once a week, the medication behavior intervention platform may send the behavior data collection prompt to the hypertensive a at seven pm five times a week from july to august. The hypertension patient A receives the behavior data acquisition prompt, fills the weekly behavior data by accessing the webpage link carried by the behavior data acquisition prompt, and returns the weekly behavior data to the medication behavior intervention platform by clicking the confirmation key of the webpage where the webpage link is located after filling. The medication behavior intervention platform receives behavior data of the hypertensive A in each week from July to August, so as to obtain the behavior data of the hypertensive A in eight weeks from July to August.
S102, learning the behavior data through a long-time memory network LSTM to obtain a medication behavior probability sequence of the (n + 1) th time period.
In some possible embodiments, the medication behavior intervention platform may first construct an LSTM network model, sort the behavior data within a preset duration according to a preset order to obtain a sorted behavior data sequence, and input the behavior data sequence into the LSTM. And learning the medication behavior type of the next time period corresponding to the behavior data of each time period in the n time periods by using a multi-classification problem of multiple medication behavior types as a learning task through the LSTM, thereby obtaining the medication behavior probability sequence of the (n + 1) th time period. The preset sequence can be the sequence from morning to evening, the medication behavior types can be four types including intentionally stopping medication, forgetting to take the medication for 1 time, forgetting to take the medication for 2-3 times and forgetting to take the medication for more than 3 times, and the preset sequence can be determined according to actual application scenes without limitation. The time period may be a day, a week, etc., and may be determined according to an actual application scenario, which is not limited herein.
For example, the medication behavior intervention platform sorts the behavior data (life behavior data, medication behavior type, etc.) of 7 months and 4 weeks according to the time sequence of the first week, the second week, the third week and the fourth week to obtain a behavior data sequence. And then inputting the behavior data sequence into the LSTM, learning a second week medication behavior type corresponding to the behavior data of a first week in 4 weeks, … and a fourth week medication behavior type corresponding to the behavior data of a third week by taking a multi-classification problem of four medication behavior types including intentionally stopping taking the medicine, forgetting taking the medicine for 1 time, forgetting taking the medicine for 2-3 times and forgetting taking the medicine for more than 3 times as a learning task through the LSTM, and obtaining a fifth week medication behavior probability sequence.
In some possible implementations, the bidirectional LSTM provided by the embodiments of the present application includes a plurality of LSTM memory units, wherein each parameter in the LSTM memory unit can be determined by the following equations 1 to 5.
Wherein equations 1 to 5 satisfy:
it＝σ(Wixt+Uiht-1) (1)
ft＝σ(Wfxt+Ufht-1) (2)
ot＝σ(Woxt+Uoht-1) (3)
in the above equations 1 to 5, σ (x) and
are all nonlinear activation functions.
Wherein σ (x) is a sigmoid function and satisfies: σ (x) ═ 1+ exp (-x)-1。
in the present application, the behavior data of n time periods are serially connected in a predetermined time sequence (from morning to evening) in a sorted manner to form a behavior data sequence, and the behavior data is input to the LSTM, so that the behavior data input at a certain time t corresponds to a certain time period of the n time periods, and therefore, in the above equations 1 to 5, the variable t may correspond to the time period. x is the number oftIt represents the behavior data corresponding to the time period entered at the time t. i.e. it，ftAnd otAnd the input gate, the memory gate and the output gate respectively represent the time t, and output the medication behavior probability sequence of the target patient in the next time period corresponding to the behavior data input at the time t. For example, if xtRepresenting the entered behavioral data of the target patient in the first week, otRepresenting the probability sequence of the medication behavior of the target patient in the second week. The input gate, the memory gate and the output gate are collectively referred to as logic gates of the LSTM memory cell. c. CtInformation representing the target patient represented by the behavioral data entered at time t may be referred to for convenience as information of the LSTM memory unit at the current time t.
In the LSTM network provided by the present application, there is an input x corresponding to each time segment at the current time t for each of the information of the LSTM memory unit at the current time t and the calculation of the probability output by each logic gate (input gate, output gate, memory gate) in the LSTM memory unittAnd an implicit variable h of the previous time period corresponding to the respective time period at the previous time t-1t-1The weight transfer matrix W. E.g. correspond to itW of (2)iCorresponds to ftW of (2)fCorresponds to otW of (2)oAnd corresponds to ctW of (2)cAnd the like. Wherein the above hidden variable ht-1Can be determined by the output of the output gate and the memory cell at the last time t-1. The hidden variable is an invisible state variable and is a parameter relative to an observable variable. Observable variables may include features that can be derived directly from the image to be detected, implied variables are variables that are one layer higher than the abstract concept of these observable variables, and implied variables are variables that can be used to controlA parameter of the change in the observable variable is measured.
S103, determining the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence.
In some feasible embodiments, one medication behavior probability in the medication behavior probability sequence corresponds to one medication behavior, and the medication behavior intervention platform determines the medication behavior corresponding to the maximum medication behavior probability in the medication behavior probability sequence as the type of the medication behavior in the (n + 1) th time period.
For example, the medication behavior probability sequence of the fifth week obtained in step S102 is 0.1, 0.2, 0.1, 0.6, and the medication behaviors corresponding to the medication behavior probabilities in the medication behavior probability sequence are intentional stop, forget to take medication 1 time, forget to take medication 2-3 times, and forget to take medication 3 times or more, then the medication behavior intervention platform determines that the medication behavior type of the fifth week is the medication behavior type corresponding to forgetting to take medication 3 times or more corresponding to 0.6.
And S104, determining a target intervention strategy according to the medication behavior type and the non-medication behavior data, and sending medication reminding information to a target patient based on the target intervention strategy.
Before executing step S104, the medication behavior intervention platform obtains a plurality of medication behavior intervention schemes, each intervention scheme including a medication behavior type, non-medication behavior data of the patient, and an intervention policy, and generates a plurality of policy tags or a combination policy tag according to the medication behavior type and the non-medication behavior data of the patient in each intervention scheme. For example, if the type of medication behavior of a hypertensive in the first intervention scenario is forgetting to take medication 1 time, and the non-medication behavior data includes motor behavior and no change in blood pressure level, the combined strategy label generated in the above manner is "forgetting to take medication 1 time-motor-no change in blood pressure level". And then, the medication behavior intervention platform obtains an intervention strategy set according to the intervention strategy in each intervention scheme and the strategy label carried by the intervention strategy.
It should be noted that the policy tag and the self-management tag generated by the medication behavior intervention platform are in the same form, in other words, if the policy tag is a combined tag, the self-management tag is also a combined tag; if the policy tag is in the form of a plurality of tags, the self-management tag is also in the form of a plurality of tags. The present application illustrates that both policy and self-administration tags are multiple tags.
In some feasible embodiments, the medication behavior intervention platform generates a self-management label of a target patient according to the medication behavior type and the non-medication behavior data, calculates the matching degree between the self-management label and the strategy labels of each intervention strategy in the intervention strategy set to obtain a plurality of matching values, and presets the intervention strategy set to comprise a plurality of intervention strategies, wherein one intervention strategy carries at least one strategy label; and determining the intervention strategy corresponding to the maximum value in the matching values as a target intervention strategy.
Specifically, the medication behavior intervention platform can generate a self-management tag according to the type of medication behavior, and life style data and body symptom data in the non-medication behavior data. For example, if the type of medication behavior of the target patient is intentional cessation, the lifestyle data includes non-smoking behavior and drinking behavior, and the physical symptom data is large in the range of variation in blood pressure level, the self-management label generated from the above data may be intentional cessation, drinking, and large in the range of variation in blood pressure level. If the self-management label of the target patient is A, B, C and the policy labels of the ith intervention policy are a, b, and c, the calculation formula of the matching degree between the self-management label and the policy label of the ith intervention policy may be the sum of the matching degrees between the self-management label A, B, C and the policy labels a, b, and c of the ith intervention policy, respectively. The matching degree between a single self-management tag and a single policy tag can be obtained through a preset matching degree table (as shown in table 1, table 1 is a matching table of self-management tags and policy tags).
TABLE 1
A, B, C, D, E and … … in the self-management labels respectively represent that medicine is intentionally stopped, medicine is forgotten to be taken for more than 3 times, medicine is forgotten to be taken for 2-3 times, medicine is forgotten to be taken for 1 time, the blood pressure variation range is large, … …, a, b, c and … … in the policy labels respectively represent that medicine is intentionally stopped, medicine is forgotten to be taken for more than 3 times, medicine is forgotten to be taken for 2-3 times and … …, the matching degrees between policy label a and self-management label A, B, C, D, E are respectively 10, 0 and 5, the matching degrees between policy label b and self-management label A, B, C, D, E are respectively 10, 0 and 4, and the matching degrees between policy label c and self-management label A, B, C, D, E are respectively 10, 0 and 3.
For example, suppose that the self-management label of hypertensive X includes forgetting to take medicine 2-3 times, drinking alcohol, and large variation range of blood pressure level, and the strategy label of the ith intervention strategy includes intentional medicine withdrawal, smoking, and large variation range of blood pressure level. The medication behavior intervention platform obtains the matching degrees of 0, 0 and 3 between the self-management label 'forget to take medicine for 2-3 times' and the policy label 'intentionally stop medicine', 'smoke' and 'large blood pressure level variation range' respectively by searching a preset matching degree table (shown in table 1), the matching degrees of 0, 0 and 2 between the self-management label 'drink' and the policy label 'intentionally stop medicine', 'smoke' and 'large blood pressure level variation range' respectively, the matching degrees of 5, 1 and 10 between the self-management label 'large blood pressure level variation range' and the policy label 'intentionally stop medicine', 'smoke' and 'large blood pressure level variation range' respectively, and then the matching degree between the self-management label and the policy label of the ith intervention policy is 21. And calculating to obtain the matching degree between the self-management label and the strategy label of each intervention strategy in the intervention strategy set according to the mode so as to obtain a plurality of matching degree values, and determining the intervention strategy corresponding to the maximum value in the plurality of matching degree values as the target intervention strategy.
In some possible embodiments, the targeted intervention strategy includes a medication reminder approach and a medication reminder frequency. And the medicine taking behavior intervention platform sends medicine taking reminding information to the target patient in a medicine taking reminding mode according to the medicine taking reminding frequency in a preset time period, wherein the medicine taking reminding mode comprises a text prompt or a voice prompt. Optionally, the text prompt may include a short message prompt, a mail prompt, an APP prompt, etc., and the voice prompt may include a telephone prompt.
For example, the medication behavior intervention platform sends short messages containing contents of reminding medication and hypertension complications to the hypertension patient a in the form of short messages at nine morning and seven evening respectively.
In the application, the medication behavior intervention platform can obtain the medication behavior probability sequence of the target patient in the (n + 1) th time period through the LSTM according to the behavior data of the target patient in the n time periods, further determine the medication behavior of the target patient in the (n + 1) th time period, determine the target intervention strategy according to the non-medication behavior data in the behavior data of the target patient in the n time periods and the medication behavior of the (n + 1) th time period, and send medication reminding information to the target patient based on the target intervention strategy, so that intervention is more timely, and the medication dependency of the patient is improved.
Please refer to fig. 3, which is another flow chart of the intervention method for the medication behavior of the patient provided by the present application. As shown in fig. 3, the method provided by the present application may include the following steps:
s201, behavior data of the target patient in a preset time length is obtained, the behavior data comprises non-medication behavior data, and the preset time length comprises n time periods.
S202, learning the behavior data through a long-time memory network LSTM to obtain a medication behavior probability sequence of the (n + 1) th time period.
And S203, determining the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence.
In some possible embodiments, the implementation manners performed in the steps S201 to S203 may refer to the implementation manners provided in the steps S101 to S103 in the embodiment shown in fig. 2, and are not described herein again.
S204, determining a target intervention strategy according to the medication behavior type and the non-medication behavior data, wherein the target intervention strategy comprises a medication reminding mode and a medication reminding frequency.
In some possible embodiments, before performing step S204, the medication behavior intervention platform may construct a knowledge graph of a certain disease from clinical medical knowledge and behavioral data of a certain disease patient, the knowledge graph comprising a plurality of intervention strategies for the certain disease patient, each intervention strategy comprising at least one combined label for the patient, wherein the combined labels may be represented in an entity-attribute form. The entities may include lifestyle, disease metrics, medication behavior types, etc., and the attributes of the lifestyle entities may include smoking, drinking, exercise, etc. Here, the disease measurement index is exemplified by a patient with hypertension, the disease measurement index may be a blood pressure level change, the attributes of the blood pressure level change include a systolic lower limit value, a systolic upper limit value, a diastolic lower limit value and a diastolic upper limit value, and the attributes of the medication behavior type may include intentional withdrawal, forgetting to take medication 1 time, forgetting to take medication 2-3 times and forgetting to take medication more than 3 times. Here, the attribute of the entity may be represented in the form of a score, for example, an attribute of a medication behavior type: intentionally stopping, forgetting to take the medicine 1 time, forgetting to take the medicine 2-3 times, and forgetting to take the medicine more than 3 times, which can be respectively represented by scores of 0, 3, 2, and 1. And then, traversing the combined label of each intervention strategy in the knowledge graph by the medication behavior intervention platform according to the non-medication behavior data and the medication behavior type of the target patient until the intervention strategy with the highest matching degree with the non-medication behavior data and the medication behavior type of the target patient is found, and determining the intervention strategy as the target intervention strategy.
S205, in a preset time period, sending medication reminding information to a target patient in a medication reminding mode according to medication reminding frequency.
The preset time period and the medication reminding frequency can be determined by the time and the medication frequency of the patient, and in addition, the medication reminding information comprises information for reminding the patient to take the medicine, and also comprises education contents provided by non-medication behavior data and medication behavior types of the target patient.
For example, if the target patient takes the medication three times a day before meals, the non-medication behavior data includes smoking, and the type of medication behavior in the next week is intentional cessation, the medication behavior intervention platform alerts the target patient to take the medication on the phone at eight am, ten am, and six pm, respectively, and teaches the target patient about medication compliance.
In the application, the medication behavior intervention platform may obtain, through the LSTM, a medication behavior probability sequence of the target patient in the (n + 1) th time period according to the behavior data of the target patient in the n time periods, and further determine the medication behavior of the target patient in the (n + 1) th time period, and determine the target intervention policy according to the non-medication behavior data of the target patient in the behavior data of the n time periods and the medication behavior of the (n + 1) th time period. The target intervention strategy comprises a medication reminding frequency and a medication reminding mode, and medication reminding information is sent to a target patient in the medication reminding mode according to the medication reminding frequency in a preset time period, so that intervention is more timely, and medication compliance of the patient is improved. In addition, the target intervention strategies are determined according to the behavior data of the patient, and the medication reminding information in the target intervention strategies comprises the patient education content, so that the personalized intervention on the medication behaviors of the patient is realized, the pressure of a doctor is relieved, and the management quality of chronic diseases is improved.
Based on the description of the above method embodiment, the present application further provides a patient medication behavior intervention device, which may be a medication behavior intervention platform in the above method embodiment. Fig. 4 is a schematic structural diagram of a patient medication behavior intervention device provided by the present application. As shown in fig. 4, the patient medication behavior intervention device 4 may include: a behavior data acquisition module 41, a probability sequence learning module 42, a behavior type determination module 43, and a determination transmission module 44.
A behavior data obtaining module 41, configured to obtain behavior data of a target patient within a preset time period, where the behavior data includes non-medication behavior data, the preset time period includes n time periods, and n is a natural number;
a probability sequence learning module 42, configured to learn the behavior data through a long-term and short-term memory network LSTM to obtain a medication behavior probability sequence in an n +1 th time period;
a behavior type determining module 43, configured to determine a medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence;
and the determining and sending module 44 is configured to determine a target intervention policy according to the medication behavior type and the non-medication behavior data, and send medication reminding information to the target patient based on the target intervention policy.
In some possible embodiments, the target intervention strategy includes a medication reminding mode and a medication reminding frequency;
the determination sending module 44 is configured to send the medication reminding information to the target patient in the medication reminding manner according to the medication reminding frequency within a preset time period;
the medication reminding mode comprises a text prompt or a voice prompt.
In some possible embodiments, the above probability sequence learning module 42 is configured to:
sequencing the behavior data within the preset duration according to a preset sequence to obtain a sequenced behavior data sequence, and inputting the behavior data sequence into the LSTM;
and learning the medication behavior type of the next time period corresponding to the behavior data of each time period in the n time periods by using a multi-classification problem of a plurality of medication behavior types as a learning task through the LSTM.
In some possible embodiments, one medication behavior probability in the medication behavior probability sequence corresponds to one medication behavior type;
the behavior type determining module 43 is configured to determine the medication behavior type corresponding to the maximum medication behavior probability in the medication behavior probability sequence as the medication behavior type of the target patient in the (n + 1) th time period.
In some possible embodiments, the determining and sending module 44 includes:
a label generating unit 441, configured to generate a self-management label of the target patient according to the medication behavior type and the non-medication behavior data;
a matching degree calculating unit 442, configured to calculate a matching degree between the self-management tag and a policy tag of each intervention policy in an intervention policy set to obtain a plurality of matching degree values, where the preset intervention policy set includes a plurality of intervention policies, and one intervention policy carries at least one policy tag;
a policy determining unit 443, configured to determine an intervention policy corresponding to a maximum value of the plurality of matching values as the target intervention policy.
In some possible embodiments, the behavior data obtaining module 41 includes:
a sending prompting unit 411, configured to send a behavior data acquisition prompt to a target patient according to a preset frequency to prompt the target patient to feed back behavior data according to a target data acquisition form, where the target data acquisition form includes web link acquisition or two-dimensional code acquisition;
a receiving data unit 412, configured to receive the behavior data fed back by the target patient as the behavior data of the target patient within a preset time period.
It will be appreciated that the patient medication behavior intervention device 4 is used to implement the steps performed by the medication behavior intervention platform of the embodiment of figures 2 and 3. With regard to the specific implementation manner and corresponding advantageous effects of the functional blocks included in the patient medication behavior intervention device 4 in fig. 4, reference may be made to the specific description of the foregoing embodiments in fig. 2 and fig. 3, which are not repeated herein.
The patient medication behavior intervention device 4 in the embodiment shown in fig. 4 may be implemented by the server 500 shown in fig. 5. Please refer to fig. 5, which is a schematic structural diagram of a server provided in the present application. As shown in fig. 5, the server 500 may include: one or more processors 501, memory 502, and transceiver 503. The processor 501, memory 502, and transceiver 503 are connected by a bus 504. The transceiver 503 is configured to receive or transmit data, and the memory 502 is configured to store a computer program, where the computer program includes program instructions; the processor 501 is configured to execute the program instructions stored in the memory 502, and perform the following operations:
acquiring behavior data of a target patient within a preset time length, wherein the behavior data comprises non-medication behavior data, the preset time length comprises n time periods, and n is a natural number;
learning the behavior data through a long-time memory network LSTM to obtain a medication behavior probability sequence of the (n + 1) th time period;
determining the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence;
and determining a target intervention strategy according to the medication behavior type and the non-medication behavior data, and sending medication reminding information to the target patient based on the target intervention strategy.
In some possible embodiments, the processor 501 learns the behavior data through the long and short term memory network LSTM, and specifically performs the following operations:
sequencing the behavior data within the preset duration according to a preset sequence to obtain a sequenced behavior data sequence, and inputting the behavior data sequence into the LSTM;
and learning the medication behavior type of the next time period corresponding to the behavior data of each time period in the n time periods by using a multi-classification problem of a plurality of medication behavior types as a learning task through the LSTM.
In some possible embodiments, one medication behavior probability in the medication behavior probability sequence corresponds to one medication behavior type;
the processor 501 determines the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence, and specifically executes the following operations:
and determining the medication behavior type corresponding to the maximum medication behavior probability in the medication behavior probability sequence as the medication behavior type of the target patient in the (n + 1) th time period.
In some possible embodiments, the processor 501 determines the target intervention policy according to the medication behavior type and the non-medication behavior data, and specifically performs the following operations:
generating a self-management label of the target patient according to the medication behavior type and the non-medication behavior data;
calculating the matching degree of the self-management label and the strategy labels of each intervention strategy in an intervention strategy set to obtain a plurality of matching degree values, wherein the preset intervention strategy set comprises a plurality of intervention strategies, and one intervention strategy carries at least one strategy label;
and determining the intervention strategy corresponding to the maximum value in the plurality of matching values as the target intervention strategy.
In some possible embodiments, the target intervention strategy includes a medication reminding mode and a medication reminding frequency;
the processor 501 sends medication reminding information to the target patient based on the target intervention strategy, and specifically performs the following operations:
sending the medication reminding information to the target patient in the medication reminding mode according to the medication reminding frequency in a preset time period;
the medication reminding mode comprises a text prompt or a voice prompt.
In some possible embodiments, the processor 501 obtains the behavior data of the target patient within a preset time period, and specifically performs the following operations:
sending a behavior data acquisition prompt to a target patient according to a preset frequency to prompt the target patient to feed back behavior data according to a target data acquisition form, wherein the target data acquisition form comprises webpage link acquisition or two-dimensional code acquisition;
and receiving the behavior data fed back by the target patient as the behavior data of the target patient within a preset time length.
Further, here, it is to be noted that: the present application further provides a computer-readable storage medium, and the computer-readable storage medium stores the aforementioned computer program executed by the patient medication behavior intervention device 4, and the computer program includes program instructions, and when the processor executes the program instructions, the description of the patient medication behavior intervention method in the embodiment corresponding to fig. 2 or fig. 3 can be performed, and therefore, the description thereof will not be repeated here. In addition, the beneficial effects of the same method are not described in detail. For technical details not disclosed in embodiments of the computer-readable storage medium referred to in the present application, reference is made to the description of embodiments of the method of the present application. As an example, program instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or distributed across multiple sites and interconnected by a communication network, which may comprise a block chain system.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The method and the related device provided by the application are described by referring to the method flow chart and/or the structure schematic diagram provided by the application, and each flow and/or block of the method flow chart and/or the structure schematic diagram and the combination of the flow and/or block in the flow chart and/or the block diagram can be realized by computer program instructions. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block or blocks.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (10)
1. A method of intervening in a medication act of a patient, comprising:
acquiring behavior data of a target patient within a preset time length, wherein the behavior data comprises non-medication behavior data, the preset time length comprises n time periods, and n is a natural number;
learning the behavior data through a long-time memory network LSTM to obtain a medication behavior probability sequence of the (n + 1) th time period;
determining the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence;
and determining a target intervention strategy according to the medication behavior type and the non-medication behavior data, and sending medication reminding information to the target patient based on the target intervention strategy.
2. The method of claim 1, wherein learning the behavior data through a long-term memory network (LSTM) comprises:
sequencing the behavior data in the preset duration according to a preset sequence to obtain a sequenced behavior data sequence, and inputting the behavior data sequence into the LSTM;
and learning the medication behavior type of the next time period corresponding to the behavior data of each time period in the n time periods by taking a multi-classification problem of a plurality of medication behavior types as a learning task through the LSTM.
3. The method of claim 1 or 2, wherein a medication behavior probability in the sequence of medication behavior probabilities corresponds to a type of medication behavior;
the determining the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence comprises:
and determining the medication behavior type corresponding to the maximum medication behavior probability in the medication behavior probability sequence as the medication behavior type of the target patient in the (n + 1) th time period.
4. The method of claim 1 or 3, wherein determining a target intervention strategy based on the medication behavior type and the non-medication behavior data comprises:
generating a self-management label of the target patient according to the medication behavior type and the non-medication behavior data;
calculating the matching degree of the self-management tag and the strategy tags of each intervention strategy in an intervention strategy set to obtain a plurality of matching degree values, wherein the preset intervention strategy set comprises a plurality of intervention strategies, and one intervention strategy carries at least one strategy tag;
and determining the intervention strategy corresponding to the maximum value in the plurality of matching values as the target intervention strategy.
5. The method of claim 1 or 4, wherein the target intervention strategy comprises a medication reminding mode and a medication reminding frequency;
the sending medication reminding information to the target patient based on the target intervention strategy comprises:
within a preset time period, sending the medication reminding information to the target patient in the medication reminding mode according to the medication reminding frequency;
the medication reminding mode comprises a text prompt or a voice prompt.
6. The method according to any one of claims 1-5, wherein the obtaining of the behavior data of the target patient within a preset time period comprises:
sending a behavior data acquisition prompt to a target patient according to a preset frequency to prompt the target patient to feed back behavior data according to a target data acquisition form, wherein the target data acquisition form comprises webpage link acquisition or two-dimensional code acquisition;
and receiving the behavior data fed back by the target patient as the behavior data of the target patient within a preset time length.
7. A patient medication behavior intervention device, comprising:
the behavior data acquisition module is used for acquiring behavior data of a target patient within a preset time length, wherein the behavior data comprises non-medication behavior data, the preset time length comprises n time periods, and n is a natural number;
the probability sequence learning module is used for learning the behavior data through a long-time memory network LSTM to obtain a medication behavior probability sequence in the (n + 1) th time period;
the behavior type determining module is used for determining the medication behavior type of the target patient in the (n + 1) th time period according to the medication behavior probability sequence;
and the determining and sending module is used for determining a target intervention strategy according to the medication behavior type and the non-medication behavior data and sending medication reminding information to the target patient based on the target intervention strategy.
8. The apparatus of claim 7, wherein the target intervention strategy comprises a medication reminding mode and a medication reminding frequency;
the determination sending module is used for sending the medication reminding information to the target patient in the medication reminding mode according to the medication reminding frequency in a preset time period;
the medication reminding mode comprises a text prompt or a voice prompt.
9. A server, comprising a processor, a memory and a transceiver, the processor, the memory and the transceiver being interconnected, wherein the transceiver is configured to receive or transmit data, the memory is configured to store program code, and the processor is configured to invoke the program code to perform a patient medication behavior intervention method according to any of claims 1-6.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which is executed by a processor to implement the method of intervention in a medication behavior of a patient according to any of claims 1-6.
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| CN202010940492.8A CN112037932A (en) | 2020-09-09 | 2020-09-09 | Patient medication behavior intervention method and device, server and storage medium |
| PCT/CN2020/124261 WO2021151329A1 (en) | 2020-09-09 | 2020-10-28 | Patient medication behavior intervention method and apparatus, and server and storage medium |
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| WO2022205601A1 (en) * | 2021-03-30 | 2022-10-06 | 平安科技（深圳）有限公司 | Dynamic intervention method and apparatus for treatment strategy, electronic device and storage medium |
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