CN113707262A

CN113707262A - Medicine use recommendation method and device, computer equipment and storage medium

Info

Publication number: CN113707262A
Application number: CN202111013822.XA
Authority: CN
Inventors: 程吉安
Original assignee: Ping An Medical and Healthcare Management Co Ltd
Current assignee: Shenzhen Ping An Medical Health Technology Service Co Ltd
Priority date: 2021-08-31
Filing date: 2021-08-31
Publication date: 2021-11-26

Abstract

The embodiment of the application belongs to the field of artificial intelligence, is applied to the field of medical treatment, and relates to a medicine use recommendation method which comprises the steps of detecting the pressure distribution information characteristics of a medicine based on a medicine monitoring sensor when a medicine recommendation instruction is received, and acquiring the recommended medicine taking time period and the recommended medicine taking amount of a user according to the pressure distribution information characteristics; acquiring group index parameters of the medicines based on a preset knowledge base; collecting symptom data of the user, and calculating individual index parameters of the user according to the group index parameters, the symptom data, the recommended medication period and the recommended medication amount; and generating a drug recommendation scheme of the user according to the individual index parameters. The application also provides a medicine use recommendation device, computer equipment and a storage medium. In addition, the application also relates to a block chain technology, and the medicine recommendation scheme can be stored in the block chain. The application realizes accurate recommendation of the medicine use of the user.

Description

Medicine use recommendation method and device, computer equipment and storage medium

Technical Field

The application relates to the technical field of artificial intelligence, in particular to a medicine use recommendation method and device, computer equipment and a storage medium.

Background

Chronic disease management is an important mode of improving the health status of patients. The personalized scheme of chronic disease medication is difficult to formulate and finish through a general formula because of personal constitution difference, living habit difference and mutual left and right difference of combined medication among patients. Moreover, the commonly used scheme for individually adjusting the dosage of the medicine is often obtained after the patient is subjected to the in-hospital blood drawing test, so that the cost is high, and the experience of the patient is poor. In addition, the actual medicine taking time and scene of the patient can also influence the medicine effect. At present, no personalized medicine taking scheme exists, and the life habit difference and the combined medicine taking condition of a patient can be simultaneously covered. Eventually, it leads to a problem that the patient medication recommendation is not accurate enough.

Disclosure of Invention

The embodiment of the application aims to provide a medicine use recommendation method, a medicine use recommendation device, computer equipment and a storage medium, so as to solve the technical problem that the current medicine use recommendation of a patient is not accurate enough.

In order to solve the above technical problem, an embodiment of the present application provides a method for recommending drug use, which adopts the following technical solutions:

when a medicine recommendation instruction is received, detecting the pressure distribution information characteristics of the medicine based on a medicine monitoring sensor, and acquiring the recommended medicine taking time period and the recommended medicine taking amount of a user according to the pressure distribution information characteristics;

acquiring group index parameters of the medicines based on a preset knowledge base;

collecting symptom data of the user, and calculating individual index parameters of the user according to the group index parameters, the symptom data, the recommended medication period and the recommended medication amount;

and generating a drug recommendation scheme of the user according to the individual index parameters.

Further, the step of detecting the pressure distribution information characteristic of the drug based on the drug monitoring sensor comprises:

acquiring the dot matrix distribution of the medicines on the medicine monitoring sensor;

and acquiring the static state pressure distribution area, the static state pressure distribution shape, the static state pressure distribution gradient and the pressure change curve during medicine taking of the medicine according to the dot matrix distribution, and combining the static state pressure distribution area, the static state pressure distribution shape, the static state pressure distribution gradient and the pressure change curve during medicine taking into the pressure distribution information characteristic.

Further, the step of obtaining the recommended medication period and the recommended medication amount of the user according to the pressure distribution information characteristics includes:

identifying the taking time of the medicine and the weight difference value of the medicine before and after the taking time based on the pressure distribution information characteristics;

and acquiring the specification information of the medicine, and determining a recommended medicine taking time period and a recommended medicine dosage which are matched with the taking time and the weight difference value simultaneously according to the specification information.

Further, after the step of determining the recommended medication period and the recommended medication amount which are matched with the taking time and the weight difference value simultaneously according to the specification information, the method further includes:

acquiring prescription information of the user, and generating an expected event sequence of the medicine according to the prescription information;

calculating the estimated dosage of the user based on the expected event sequence to obtain an estimated measurement value;

and when the estimated measurement value is larger than zero, adjusting the recommended medication time interval and the recommended medication amount according to the prescription information.

Further, the step of calculating the individual index parameter of the user according to the group index parameter, the symptom data, the recommended medication period and the recommended medication amount comprises:

and acquiring a preset likelihood function, and calculating the symptom data, the recommended medication period and the recommended medication amount according to the likelihood function and the group index parameter through a preset link equation to obtain the individual index parameter.

Further, the step of calculating the symptom data, the recommended medication period and the recommended medication amount according to the likelihood function and the group index parameter by a preset link equation to obtain the individual index parameter includes:

determining whether the user has historical symptom data and historical medication parameters, and acquiring the latest historical symptom data and the latest historical medication parameters of the user when the user has the historical symptom data and the historical medication parameters;

taking the latest historical symptom data and the latest historical medication parameters as current initialization parameters of the user;

and calculating to obtain the individual index parameter through the likelihood function and the preset link equation based on the initialization parameter in combination with the symptom data, the recommended medication period and the recommended medication amount.

Further, after the step of calculating the individual index parameter of the user according to the group index parameter, the symptom data, the recommended medication period and the recommended medication amount, the method further includes:

carrying out anonymous statistics on individual index parameters of different users according to privacy calculation to obtain a statistical result;

and updating the group index parameters based on the statistical result when a preset updating period is reached.

In order to solve the above technical problem, an embodiment of the present application further provides a medicine use recommendation device, which adopts the following technical scheme:

the detection module is used for detecting the pressure distribution information characteristics of the medicine based on the medicine monitoring sensor when a medicine recommendation instruction is received, and acquiring the recommended medicine taking time period and the recommended medicine taking amount of a user according to the pressure distribution information characteristics;

the acquisition module is used for acquiring group index parameters of the medicines based on a preset knowledge base;

the acquisition module is used for acquiring symptom data of the user and calculating individual index parameters of the user according to the group index parameters, the symptom data, the recommended medication period and the recommended medication amount;

and the generation module is used for generating the medicine recommendation scheme of the user according to the individual index parameters.

In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

According to the method and the device, when a medicine recommending instruction is received, the pressure distribution information characteristics of the medicine are detected based on the medicine monitoring sensor, and the recommended medicine taking time period and the recommended medicine using amount of the user are obtained according to the pressure distribution information characteristics, so that the recommended medicine taking time period and the recommended medicine using amount of the user can be obtained in time; then, acquiring group index parameters of the medicine based on a preset knowledge base; collecting symptom data of the user, and calculating individual index parameters of the user according to the group index parameters, the symptom data, the recommended medication period and the recommended medication amount, so that the individual index parameters personalized by the user can be accurately obtained, and personalized medicine use recommendation can be carried out on the user according to the individual index parameters; and then, according to the individual index parameters, a medicine recommendation scheme of the user is generated, and finally accurate recommendation of the medicine using mode of the user is achieved, so that the recommended medicine dose and the medicine time period are more in line with the actual requirements of the user, and the medicine use recommendation accuracy and recommendation efficiency are improved.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of a drug use recommendation method according to the present application;

FIG. 3 is a parameter curve simulation plot generated from individual index parameters and population index parameters;

FIG. 4(a) is a first graph of efficacy of a user taking the same dose at the same interval based on a population index parameter;

FIG. 4(b) is a second graph of efficacy of the same dosage taken by the user at the same interval based on the individual index parameter;

FIG. 5 is a schematic diagram of the structure of one embodiment of a drug use recommendation device according to the present application;

FIG. 6 is a schematic block diagram of one embodiment of a computer device according to the present application.

Reference numerals: the system comprises a drug use recommending device 300, a detection module 301, an acquisition module 302, an acquisition module 303 and a generation module 304.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that, the medicine usage recommendation method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the medicine usage recommendation apparatus is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow diagram of one embodiment of a method of drug use recommendation in accordance with the present application is shown. The medicine use recommendation method comprises the following steps:

step S201, when a medicine recommending instruction is received, detecting the pressure distribution information characteristics of a medicine based on a medicine monitoring sensor, and acquiring a recommended medicine taking time period and a recommended medicine taking amount of a user according to the pressure distribution information characteristics;

in this embodiment, the medicine recommendation instruction is a request instruction of the received medicine use recommendation. When a medicine recommending instruction is received, acquiring the pressure distribution information characteristic of a medicine corresponding to the medicine recommending instruction based on a medicine monitoring sensor, wherein the pressure distribution information characteristic is the pressure information of the medicine on the medicine monitoring sensor. According to the pressure distribution information characteristics, the weight difference value of the medicine placed on and taken down from the medicine monitoring sensor can be obtained, then the specification information of the medicine is called, and the recommended medicine taking time period and the recommended medicine taking amount of the user using the medicine are obtained according to the specification information and the weight difference value. Wherein the specification information of the medicine is effective component information of the medicine.

Step S202, acquiring group index parameters of the medicine based on a preset knowledge base;

in this embodiment, the predetermined knowledge base stores a model of pharmacokinetics of a drug metabolic pathway and parameters, the model is stored in the predetermined knowledge base in the form of a function code, and the parameters are stored in the predetermined knowledge base in the form of a dictionary. And inputting the serial number of the medicine into the preset knowledge base, so that the group index parameter corresponding to the associated medicine can be obtained according to the serial number of the medicine. The population index parameters are the common parameters of pharmacokinetics-pharmacodynamics, including biological half-life cycle, apparent distribution volume, area under blood concentration time curve and the like.

Step S203, collecting symptom data of the user, and calculating individual index parameters of the user according to the group index parameters, the symptom data, the recommended medication period and the recommended medication amount;

in this embodiment, the symptom data is basic symptom information of the user, and includes data of blood sugar, blood pressure, and the like of the user. According to wearable equipment, like equipment such as intelligent bracelet, intelligent wrist-watch or rhythm of the heart bandage, gather user's symptom data in the preset time quantum, obtain this symptom data. And when the symptom data, the recommended medication period and the recommended medication amount of the user are obtained, calculating the individual index parameters of the user according to the group index parameters, the symptom data, the recommended medication period and the recommended medication amount. The individual index parameter is an individualized index parameter, the content of the individual index parameter is the same as that of the group index parameter corresponding to the medicine, but the actual value is different, and the individual index parameter is more suitable for the current user individuals relative to the group index parameter. Specifically, when symptom data, a recommended medication period and a recommended medication amount of a user are obtained, the group index parameter, the symptom data, the recommended medication period and the recommended medication amount are calculated through MCMC (Markov Chain Monte Carlo method) to obtain an individual index parameter of the user.

And step S204, generating a medicine recommendation scheme of the user according to the individual index parameters.

In this embodiment, when obtaining the individual index parameter, the individual index parameter is input into a preset function of the pharmacokinetic-pharmacodynamic model, and the symptom improvement condition of the user is predicted based on the individual index parameter according to the pharmacodynamic function, so as to obtain a pharmacodynamic prediction curve. And acquiring a target symptom range preset by the user, and determining a medicine recommendation scheme of the user according to the target symptom range and the pesticide effect prediction curve. Specifically, whether the pesticide effect prediction curve is in the target symptom range or not is determined, if the pesticide effect prediction curve is not in the target symptom range, the maximum difference value or the average value of the target symptom range and the pesticide effect prediction curve is obtained, and a medicine recommendation scheme is obtained according to a numerical solution of a differential equation; and if the efficacy prediction curve is in the target symptom range, taking the recommended medication period and the recommended dosage of the current medicine as the medicine recommendation scheme of the current user.

Taking drug recommendation for a Parkinson patient as an example, acquiring tremor conditions (namely symptom data) of the user through an intelligent watch or a bracelet, acquiring recommended drug consumption and recommended drug consumption time period of the user required to take a drug through a drug monitoring platform, and acquiring individual index parameters of the user according to the recommended drug consumption, the recommended drug consumption time period and the symptom data; and obtaining the group index parameters of the medicine according to a preset knowledge base. And then, inputting the individual index parameters and the group index parameters into a pharmacokinetic-pharmacodynamic model, and simulating according to the pharmacokinetic-pharmacodynamic model to obtain a first parameter change curve corresponding to the individual index parameters and a second parameter change curve corresponding to the group index parameters.

As shown in fig. 3 below, fig. 3 is a parameter curve simulation diagram generated according to an individual index parameter and a group index parameter, in which a solid line is a first parameter variation curve, a dotted line is a second parameter variation curve, and discrete points are actual symptom severity curves of a user. Fig. 4 is a graph showing the effect of the same dose of medicine taken by the user and the effect obtained after the same interval time, fig. 4(a) is a graph showing the first effect of the same dose taken by the user at the same interval time based on the population index parameter, and fig. 4(b) is a graph showing the second effect of the same dose taken by the user at the same interval time based on the individual index parameter. Wherein the y-axis is the severity of the tremor symptoms associated with parkinson, and the default value of the acceptable range of symptom severity (i.e., the target symptom range) is set to about 1. Fig. 4(a) shows the efficacy curve according to the non-personalized parameters (i.e. the population index parameters), where tremor symptoms greater than 1 frequently occur, i.e. it indicates that the actual severity of the symptoms fluctuates greatly according to the efficacy obtained by the population index parameters under the currently set dosage and administration time, and the dosage and administration time need to be adjusted; fig. 4(b) shows that after the user takes the same medicine-taking dosage at the same interval time according to the individual index parameter, the tremor symptom of the user is already steadily lower than 1, which means that under the currently set medicine-taking dosage and medicine-taking time, the medicine effect estimated according to the individual index parameter is close to the actual symptom severity of the user, and the medicine-taking dosage and medicine-taking time of the user do not need to be adjusted.

It is emphasized that, to further ensure the privacy and security of the drug recommendation scheme, the drug recommendation scheme may also be stored in a node of a blockchain.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

According to the method and the device, accurate recommendation of the medicine using mode of the user is achieved, the recommended medicine dose and the medicine time period are more in line with the actual requirements of the user, and the medicine using recommendation accuracy and recommendation efficiency are improved.

In some optional implementations of this embodiment, the step of detecting the pressure distribution information characteristic of the drug based on the drug monitoring sensor includes:

In this embodiment, the medicine monitoring sensor is composed of a series of dot matrixes of pressure sensors, and when a medicine is placed on the medicine monitoring sensor, the pressure distribution information characteristics of the medicine can be acquired according to the medicine monitoring sensor. Specifically, acquiring the dot matrix distribution of the current medicine on the medicine monitoring sensor, and determining the pressure range and the pressure distribution map of the medicine according to the dot matrix distribution; and acquiring the static state pressure distribution area, the static state pressure distribution shape and the static state pressure distribution gradient of the medicine in a static state and a medicine taking pressure change curve of the medicine in the medicine taking process based on the pressure range and the pressure distribution diagram. The static state pressure distribution area, the static state pressure distribution shape, the static state pressure distribution gradient and the pressure change curve of the medicine during medicine taking are combined into the pressure distribution information characteristic of the medicine.

According to the embodiment, the pressure distribution information characteristics are acquired through the dot matrix distribution, so that the pressure distribution information characteristics of the medicine can be accurately acquired, and the accuracy of medicine administration acquisition is further improved.

In some optional implementation manners of this embodiment, the step of obtaining the recommended medication period and the recommended medication amount of the user according to the pressure distribution information characteristics includes:

In this embodiment, when obtaining the pressure distribution information characteristic of the medicine, the difference between the time of taking the medicine, which is the time when the medicine is placed on the medicine monitoring sensor, and the weight of the medicine before and after the time of taking the medicine is identified based on the pressure distribution information characteristic. According to the change of the pressure of the medicine before and after the taking time, the weight difference value of the medicine before and after the taking time can be calculated through a conversion formula of the pressure and the weight. When the difference between the time of taking and the weight is obtained, the two-dimensional code on the medicine package or the medicine number for identifying the medicine is scanned, and the specification information of the medicine, such as 100 pieces/bottle (0.5 mg/piece of effective component) or 100 ml/bottle (1 mg/ml of effective component) and the like, can be obtained. And matching the weight difference value and the taking time of the medicine with the specification information according to a preset matching scheme to obtain the recommended dosage and the recommended dosage period of the medicine.

According to the method and the device, the recommended medication time period and the recommended dosage of the medicine are determined according to the pressure distribution information characteristics, and the timeliness and the accuracy of obtaining the recommended dosage and the recommended medication time period of the medicine are improved.

In some optional implementation manners of this embodiment, after the step of determining, according to the specification information, a recommended medication period and a recommended medication amount that are simultaneously matched with the taking time and the weight difference value, the method further includes:

In this embodiment, when the recommended medication period and the recommended dosage of the user are obtained, the recommended medication period and the recommended dosage of the user can be adjusted by combining the prescription information of the user, so as to obtain a more accurate recommended medication period and recommended dosage. Specifically, prescription information of a user is acquired, and an expected event sequence of medicines is generated according to the prescription information, wherein the expected event sequence is an event sequence for the user to take medicines based on the prescription information.

For example, the prescription information of the user is medication information 2 times a day and 2 tablets at a time, and the expected event sequence of the medicine generated by the prescription information is as follows: [ drug delivery- > drug amount reduction 2 tablets ] - > [ drug delivery- > drug amount reduction 2 tablets ], wherein, the square brackets indicate an event, and arrows indicate the front and back events. When the expected event sequence is obtained, if all events in the expected event sequence corresponding to the current medicine are completed, that is, the current user finishes taking the medicine according to the prescription information, and the estimated measurement value of the current medicine is zero; if the events in the expected event sequence are not completely completed, that is, the current user does not complete the currently required dosage, the estimated measurement value of the current medicine is the medicine quantity of the uncompleted events in the expected event sequence, and the estimated measurement value of the current medicine is larger than zero. For example, in the above example, only one event in the expected sequence of events corresponding to the drug is completed, the remaining events are not completed, and the estimated measurement value of the current drug is 2. And when the estimated measurement value of the current medicine is larger than zero, according to the medicine data of the prescription information, the recommended medicine consumption and the recommended medicine consumption time period of the user are adjusted through Kalman filtering to obtain the adjusted recommended medicine consumption time period and the adjusted recommended medicine consumption.

According to the method and the device, the corresponding expected event sequence is obtained through the prescription information, and then the recommended dosage and the recommended medication period of the user are adjusted according to the expected event sequence, so that the obtained recommended dosage and the recommended medication period are more accurate, and the actual requirements of the user are better met.

In some optional implementation manners of this embodiment, the step of calculating the individual index parameter of the user according to the group index parameter, the symptom data, the recommended medication period, and the recommended medication amount includes:

In this embodiment, when obtaining symptom data, recommended medication period, and recommended medication amount of a user, an individual index parameter of the user can be calculated based on MCMC (Markov Chain Monte Carlo method) by a likelihood function L (arg1, arg2| y) of the parameter. Wherein, arg1 and arg2 are parameters which can be individualized from the group index parameters, y is the symptom data of the user, and y ═ yt1, yt2 and yt3 …, yt1, yt2 and yt3 are the symptom conditions of the user at the corresponding time. By introducing the Markov process into Monte Carlo simulation through MCMC, dynamic simulation can be realized in which the sampling distribution changes as the simulation progresses. Specifically, when symptom data, a recommended medication period and a recommended medication amount of a user are obtained, an objective function value is obtained through calculation of the likelihood function, and then a Markov chain (namely a preset link equation) is constructed to enable the Markov chain to be distributed stably as posterior distribution of parameters to be estimated; then, generating a posterior distribution sample based on the Markov chain, and respectively carrying out Monte Carlo integration on the sample when the Markov chain is stable to obtain an individual index parameter of the user, wherein the individual index parameter is an individualized pharmacokinetic-pharmacodynamic parameter.

In the embodiment, the individual index parameters are calculated through the preset likelihood function, so that the symptoms of the user can be more accurately represented according to the individual index parameters, and accurate recommendation of the use of the medicine of the user is further realized.

In some optional implementation manners of this embodiment, the step of calculating the symptom data, the recommended medication period, and the recommended medication amount according to the likelihood function and the group index parameter by using a preset link equation to obtain the individual index parameter includes:

In this embodiment, in order to ensure real-time performance and accuracy of the individual index parameters of the user, the latest historical symptom data and the latest historical medication parameter of the user are used as initial values of the currently calculated individual index parameters of the user, and the individual index parameters of the current user are calculated according to the initial values. Specifically, when symptom data, a recommended medication period and a recommended medication amount are obtained, whether historical symptom data and historical medication parameters exist in the user is determined, wherein the historical medication parameters comprise the historical medication amount and the historical medication period. When the user has historical symptom data and historical medication parameters, the latest historical symptom data and the latest historical medication parameters of the user are obtained, and the latest historical symptom data and the latest historical medication parameters are used as initialization parameters. And calculating to obtain the individual index parameters of the current user through the likelihood function of the initialization parameters and the preset link equation of the MCMC based on the initialization parameters in combination with symptom data, recommended medication period and recommended medication amount.

In the embodiment, the latest historical symptom data and the latest historical medication parameter are used as the initialization parameters of the user, and the individual index parameter is calculated according to the initialization parameters, so that the convergence speed of the operation is increased, and the calculation efficiency of the individual index parameter is improved.

In some optional implementations of this embodiment, after the step of calculating the individual index parameter of the user according to the group index parameter, the symptom data, the recommended medication period, and the recommended medication amount, the method further includes:

In this embodiment, after obtaining the group index parameters, anonymous statistics may be performed on individual index parameters of different users through privacy calculation to obtain a statistical result; and updating the group index parameters of the medicine according to the statistical result. The privacy calculation includes, but is not limited to, privacy operations such as encryption and anonymity of individual index parameters of different users. And when the preset updating period is reached, updating the group index parameters according to the statistical result after the privacy calculation. Specifically, when a statistical result is obtained, updating parameters of the pharmacokinetic model in the preset knowledge base according to the statistical result, and then recalculating the group index parameter according to the pharmacokinetic model after updating the parameters to obtain the updated group index parameter. Therefore, periodic updating of the group index parameters is achieved.

According to the method and the device, the individual index parameters of different users are subjected to anonymous statistics through privacy calculation, and the group index parameters are updated according to the statistical result when the preset updating period is reached, so that the periodic updating of the group index parameters is realized, the information safety of the users is ensured, and the accuracy of medication recommendation of the users is further improved.

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 hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 5, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a medicine usage recommendation device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be applied to various electronic devices.

As shown in fig. 5, the medicine use recommendation device 300 according to the present embodiment includes: a detection module 301, an acquisition module 302, an acquisition module 303, and a generation module 304. Wherein:

the detection module 301 is configured to detect a pressure distribution information characteristic of a medicine based on a medicine monitoring sensor when a medicine recommendation instruction is received, and acquire a recommended medication period and a recommended medication amount of a user according to the pressure distribution information characteristic;

in some optional implementations of this embodiment, the detecting module 301 includes:

the acquisition unit is used for acquiring the dot matrix distribution of the medicines on the medicine monitoring sensor;

and the first confirmation unit is used for acquiring the static state pressure distribution area, the static state pressure distribution shape, the static state pressure distribution gradient and the pressure change curve during medicine taking of the medicine according to the dot matrix distribution, and combining the static state pressure distribution area, the static state pressure distribution shape, the static state pressure distribution gradient and the pressure change curve during medicine taking into the pressure distribution information characteristic.

In some optional implementations of this embodiment, the detecting module 301 further includes:

the identification unit is used for identifying the taking time of the medicine and the weight difference value of the medicine before and after the taking time based on the pressure distribution information characteristics;

and the second confirmation unit is used for acquiring the specification information of the medicine and determining the recommended medicine taking time period and the recommended medicine taking amount which are simultaneously matched with the taking time and the weight difference value according to the specification information.

The generation unit is used for acquiring prescription information of the user and generating an expected event sequence of the medicine according to the prescription information;

the first calculation unit is used for calculating the estimated medicine consumption of the user based on the expected event sequence to obtain an estimated measurement value;

and the adjusting unit is used for adjusting the recommended medication period and the recommended medication amount according to the prescription information when the estimated measurement value is larger than zero.

An obtaining module 302, configured to obtain a group index parameter of the drug based on a preset knowledge base;

The acquisition module 303 is configured to acquire symptom data of the user, and calculate an individual index parameter of the user according to the group index parameter, the symptom data, the recommended medication period, and the recommended medication amount;

in some optional implementations of this embodiment, the acquiring module 303 includes:

and the second calculation unit is used for acquiring a preset likelihood function, and calculating the symptom data, the recommended medication period and the recommended medication amount according to the likelihood function and the group index parameters through a preset link equation to obtain the individual index parameters.

In some optional implementations of this embodiment, the second calculating unit includes:

the third confirming unit is used for determining whether the user has historical symptom data and historical medication parameters, and acquiring the latest historical symptom data and the latest historical medication parameters of the user when the user has the historical symptom data and the historical medication parameters;

a fourth confirming unit, configured to use the latest historical symptom data and the latest historical medication parameter as initialization parameters of the current user;

and the third calculating unit is used for calculating the individual index parameter through the likelihood function and the preset link equation based on the initialized parameter and the symptom data, the recommended medication period and the recommended medication amount.

A generating module 304, configured to generate a drug recommendation scheme for the user according to the individual index parameter.

Taking drug recommendation for a Parkinson patient as an example, acquiring tremor conditions (namely symptom data) of the user through an intelligent watch or a bracelet, acquiring recommended drug consumption and recommended drug consumption time period of the user required to take a drug through a drug monitoring platform, and acquiring individual index parameters of the user according to the recommended drug consumption, the recommended drug consumption time period and the symptom data; and obtaining the group index parameters of the medicine according to a preset knowledge base. And then, inputting the individual index parameters and the group index parameters into a pharmacokinetic-pharmacodynamic model, and simulating according to the pharmacokinetic-pharmacodynamic model to obtain a first parameter change curve corresponding to the individual index parameters and a second parameter change curve corresponding to the group index parameters. As shown in fig. 3 below, fig. 3 is a parameter curve simulation diagram generated according to an individual index parameter and a group index parameter, in which a solid line is a first parameter variation curve, a dotted line is a second parameter variation curve, and discrete points are actual symptom severity curves of a user. Fig. 4 is a graph showing the effect of the same dose of medicine taken by the user and the effect obtained after the same interval time, fig. 4(a) is a graph showing the first effect of the same dose taken by the user at the same interval time based on the population index parameter, and fig. 4(b) is a graph showing the second effect of the same dose taken by the user at the same interval time based on the individual index parameter. Wherein the y-axis is the severity of the tremor symptoms associated with parkinson, and the default value of the acceptable range of symptom severity (i.e., the target symptom range) is set to about 1. Fig. 4(a) shows the efficacy curve according to the non-personalized parameters (i.e. the population index parameters), where tremor symptoms greater than 1 frequently occur, i.e. it indicates that the actual severity of the symptoms fluctuates greatly according to the efficacy obtained by the population index parameters under the currently set dosage and administration time, and the dosage and administration time need to be adjusted; fig. 4(b) shows that after the user takes the same medicine-taking dosage at the same interval time according to the individual index parameter, the tremor symptom of the user is already steadily lower than 1, which means that under the currently set medicine-taking dosage and medicine-taking time, the medicine effect estimated according to the individual index parameter is close to the actual symptom severity of the user, and the medicine-taking dosage and medicine-taking time of the user do not need to be adjusted.

In some optional implementations of the present embodiment, the above medicine usage recommending apparatus 300 further includes:

the statistical module is used for carrying out anonymous statistics on individual index parameters of different users according to privacy calculation to obtain statistical results;

and the updating module is used for updating the group index parameters based on the statistical result when a preset updating period is reached.

The medicine use recommending device provided by the embodiment realizes accurate recommendation of the medicine use mode of the user, so that the recommended medicine dose and the medicine time period are more in line with the actual requirements of the user, and the medicine use recommending accuracy and recommending efficiency are improved.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 6, fig. 6 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only a computer device 6 having components 61-63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system installed in the computer device 6 and various types of application software, such as computer readable instructions of a drug use recommendation method. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute computer readable instructions or process data stored in the memory 61, for example, execute computer readable instructions of the drug use recommendation method.

The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.

The computer equipment provided by the embodiment realizes accurate recommendation of the medicine using mode of the user, so that the recommended medicine dose and the medicine time period are more in line with the actual requirements of the user, and the medicine use recommendation accuracy and recommendation efficiency are improved.

The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the drug use recommendation method as described above.

The computer-readable storage medium provided by the embodiment realizes accurate recommendation of the medicine use mode of the user, so that the recommended medicine dose and the medicine time period are more in line with the actual requirements of the user, and the accuracy and the recommendation efficiency of medicine use recommendation are improved.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. A method for recommending the use of a pharmaceutical product, comprising the steps of:

2. The drug use recommendation method of claim 1, wherein the step of detecting the pressure profile information characteristic of the drug based on the drug monitoring sensor comprises:

3. The drug use recommendation method according to claim 1, wherein the step of obtaining the recommended medication period and the recommended medication amount of the user according to the pressure distribution information characteristics comprises:

4. The medication use recommendation method according to claim 3, further comprising, after the step of determining a recommended medication period and a recommended medication amount that match the taking time and the weight difference value simultaneously according to the specification information:

5. The medication use recommendation method according to claim 1, wherein the step of calculating the individual index parameter of the user from the group index parameter, the symptom data, the recommended medication period, and the recommended medication amount comprises:

6. The drug use recommendation method according to claim 5, wherein the step of calculating the symptom data, the recommended medication period and the recommended medication amount by a preset link equation according to the likelihood function and the group index parameter to obtain the individual index parameter comprises:

7. The medication use recommendation method according to claim 1, further comprising, after the step of calculating an individual index parameter of the user from the population index parameter, the symptom data, the recommended medication period, and the recommended medication amount:

8. A medication use recommendation device, comprising:

9. A computer device comprising a memory and a processor, wherein the memory has stored therein computer readable instructions which, when executed by the processor, implement the steps of the medication use recommendation method of any one of claims 1 to 7.

10. A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the steps of the drug use recommendation method of any one of claims 1-7.