WO2021179694A1 - Drug recommendation method, apparatus, computer device, and storage medium - Google Patents
Drug recommendation method, apparatus, computer device, and storage medium Download PDFInfo
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- WO2021179694A1 WO2021179694A1 PCT/CN2020/132479 CN2020132479W WO2021179694A1 WO 2021179694 A1 WO2021179694 A1 WO 2021179694A1 CN 2020132479 W CN2020132479 W CN 2020132479W WO 2021179694 A1 WO2021179694 A1 WO 2021179694A1
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- 230000036252 glycation Effects 0.000 description 2
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- 229940123208 Biguanide Drugs 0.000 description 1
- XNCOSPRUTUOJCJ-UHFFFAOYSA-N Biguanide Chemical compound NC(N)=NC(N)=N XNCOSPRUTUOJCJ-UHFFFAOYSA-N 0.000 description 1
- 206010020772 Hypertension Diseases 0.000 description 1
- 229940100389 Sulfonylurea Drugs 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- G—PHYSICS
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H70/00—ICT specially adapted for the handling or processing of medical references
- G16H70/40—ICT specially adapted for the handling or processing of medical references relating to drugs, e.g. their side effects or intended usage
Definitions
- This application relates to the field of computer technology, and in particular to a method, device, computer equipment and storage medium for drug recommendation.
- the neural network model can predict the drugs that need to be recommended to the patient based on all the characteristics of the patient, but because the neural network shares the characteristics of neurons, it can only The characteristics are explained, but a certain characteristic of the patient cannot be explained. As a result, the patient may not accept the drugs recommended by the above-mentioned model. It can be seen that the above-mentioned neural network model lacks sufficient interpretability and poor applicability. In addition, when recommending drugs to patients through a statistical model, although the statistical model has sufficient interpretability, the accuracy of the model is low.
- This application provides a drug recommendation method, device, computer device, and storage medium, which can accurately recommend drugs to users, improve the accuracy of model drug recommendation, and also increase the user stickiness of drug recommendation.
- this application provides a drug recommendation method, which includes:
- the medication index prediction model includes at least two medication index prediction networks, and one medication index prediction network is used to output the user’s information Multiple medication indicators of a user attribute information under the action of multiple drugs;
- the present application provides a drug recommendation device, which includes:
- the first acquisition module is used to acquire at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into the medication index prediction model.
- the medication index prediction model includes at least two medication index prediction networks, one medication index
- the prediction network is used to output multiple medication indicators of a user attribute information of the user under the action of multiple drugs;
- the first determination module is used to determine the medication index of each target user attribute information of the target user under the action of each drug based on the drug index prediction network in the medication index prediction model, and one type of target user attribute information corresponds to the action of a drug A medication index;
- the second determining module is configured to determine the target drug use indicators corresponding to at least two target user attribute information under any drug action based on the drug use indicator prediction model to obtain multiple target drug indicators corresponding to multiple drugs;
- the push module is used to determine the maximum target medication indicator from multiple target medication indicators, and push the target drug with the largest target medication indicator to the target user.
- this application provides a computer device, including: a processor, a memory, and a network interface;
- the processor is connected to a memory and a network interface, where the network interface is used to provide data communication functions, the memory is used to store a computer program, and the processor is used to call the computer program to execute the following methods:
- the medication index prediction model includes at least two medication index prediction networks, and one medication index prediction network is used to output the user’s information Multiple medication indicators of a user attribute information under the action of multiple drugs;
- the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when executed by a processor, the program instructions execute the following methods:
- the medication index prediction model includes at least two medication index prediction networks, and one medication index prediction network is used to output the user’s information Multiple medication indicators of a user attribute information under the action of multiple drugs;
- each medication index is determined through the medication index prediction network, and the target medication index is further determined according to each medication index, thereby improving the accuracy of the model for predicting the target medication index.
- the computer device can determine multiple target medication indicators corresponding to multiple drugs based on the medication indicator prediction model, and push the target drug with the largest target medication indicator to the target user, thereby accurately recommending medication to the user and improving The accuracy of model drug recommendation is improved, and the user stickiness of drug recommendation is also improved, and the applicability is strong.
- Figure 1 is a schematic structural diagram of the network architecture provided by this application.
- Figure 2 is a schematic flow chart of the drug recommendation method provided by the present application.
- Figure 3 is a schematic diagram of the structure of the medication index prediction model provided by this application.
- FIG. 4 is a schematic diagram of the structure of the drug recommendation device provided by the present application.
- Fig. 5 is a schematic diagram of the structure of the computer equipment provided by the present application.
- the technical solution of this application can be applied to the fields of artificial intelligence, smart city, digital medical, blockchain and/or big data technology to realize intelligent drug recommendation.
- the data involved in this application such as user attribute information, medication indicators, and/or pushed medication information, can be stored in a database, or can be stored in a blockchain, such as distributed storage through a blockchain, this application Not limited.
- the network architecture may include a server 10 and a user terminal cluster.
- the user terminal cluster may include multiple user terminals. As shown in FIG. 1, it may specifically include a user terminal 100a, a user terminal 100b, and a user terminal 100c. ..., the user terminal 100n.
- the server 10 may be an independent physical server, or it may provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks ( content delivery network (CDN), big data and artificial intelligence platform and other basic cloud computing services cloud server.
- Each user terminal in the user terminal cluster may include, but is not limited to: smart phones, tablet computers, notebook computers, desktop computers, smart speakers, smart watches, and other smart terminals.
- the computer device in the present application may be a physical terminal with a drug recommendation function.
- the physical terminal may be the server 10 as shown in FIG. 1 or a user terminal, which is not limited here.
- the user terminal 100a, the user terminal 100b, the user terminal 100c, ..., the user terminal 100n can be connected to the aforementioned server 10 respectively, so that each user terminal can interact with the server 10 through the network connection.
- the server 10 may push the target drug to the user interface of the target user's user terminal (or target user terminal for short), and then the target user can view the target drug on the user interface, where the target user terminal may be a user terminal Any user terminal in the cluster (such as the user terminal 100a).
- the application may refer to the drug recommended to the target user determined based on the drug index prediction model as the target drug, and the application may also call the model with the function of predicting the target drug index of the target user under the action of multiple drugs as the target drug. It is a predictive model of medication index.
- the drug recommendation method provided in this application may be applicable to a drug recommendation scenario for any disease, such as a diabetes drug recommendation scenario, a hypertension drug recommendation scenario, or a drug recommendation scenario for other diseases.
- the target user is a doctor
- the doctor can input the basic information of the patient into the clinical decision support system (CDSS), and the CDSS contains the above-mentioned medication index prediction model, which can output the recommended target drug based on the basic information of the patient
- the doctor can view the target drug on the user interface (the target drug here can be used as the preliminary diagnosis result), and then combine with the patient's further diagnosis results to determine the drug suitable for the patient (such as the above target drug).
- the patient can input their basic information into self-service terminals (or self-service kiosks for short) provided by hospitals, health stations, or social health institutions.
- the self-service kiosks contain the above-mentioned medication index prediction model, which can be based on
- the basic information of the patient outputs the recommended target medicine to the user interface of the self-service machine.
- the patient can view the target drug in the user interface of the self-service machine, and the subsequent patient can directly purchase the target drug, or allow the doctor to further diagnose and determine the drug suitable for the patient (such as the above target drug).
- FIG. 2 is a schematic flowchart of the drug recommendation method provided by the present application. As shown in Figure 2, the method may include the following steps S101 to S104:
- Step S101 Obtain at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into the medication index prediction model.
- the computer device may first train the medication index prediction network in the medication index prediction model (such as neural additive models (NAM)) through the sample data of at least two users.
- the medication index prediction network used to output the medication index of each user attribute information of any user under the action of multiple drugs is obtained.
- the number of medication index prediction networks in the medication index prediction model is the same as the number of user attribute information of the user. It is assumed that the medication index prediction model corresponds to a neural network.
- the neural network can contain multiple sub-neural networks.
- the medication index prediction The network can be a sub-neural network.
- the computer device can obtain sample data of at least two users.
- the sample data of at least two users is used to train the medication index prediction model
- one user corresponds to one sample data
- one sample data can include at least two types of user attribute information and the user's target sample medication index under the action of drugs.
- the target sample medication index may be the actual medication index of the user under the action of the drug.
- the drugs used by different users for the target disease can be the same or different, and the specific drugs can be determined according to the actual application scenario, which is not limited here.
- the computer device can input the sample data of at least two users into the medication index prediction model, and perform joint learning on the user attribute information corresponding to each medication index prediction network through each medication index prediction network in the medication index prediction model to obtain the prediction task.
- the computer equipment performs data cleaning and feature screening on the sample data of at least two users, and finally forms a prediction about the user attribute information of any user under the action of each drug.
- the feature data here may include, but is not limited to, the user's age, gender, and health indicators for target diseases (such as diabetes) (such as glycosylated hemoglobin value and creatinine value) and other multiple dimension feature data, which is not limited here.
- the computer device can input the sample data of at least two users into the medication index prediction model, and use the medication index prediction network in the medication index prediction model to predict the medication of each user attribute information of any user under the action of each drug.
- the index is a learning task.
- the user attribute information corresponding to each medication index prediction network is jointly learned to obtain each network parameter corresponding to each medication index prediction network, and based on each network parameter, determine the role of each user attribute information of any user in each drug The ability to follow the medication index.
- the computer device can perform data feature learning on the above-mentioned feature data based on each medication index prediction network to obtain the ability to predict the medication index of each user attribute information of any user under the action of each drug.
- the computer device can calculate the loss value corresponding to each sample data through a loss function (such as a binary cross-entropy loss function). Further, the computer equipment can iteratively update the network parameters corresponding to each medication index prediction network according to the loss value corresponding to each sample data, stop training when the loss value is basically unchanged, and use the iteratively updated network parameters as each medication
- the index predicts the final network parameters of the network.
- each medication index prediction network has the ability to predict the medication index of any user's user attribute information under the action of each drug.
- the medication index prediction model has the ability to predict any user (such as the target user) in each drug. The ability to target medication indicators under the action.
- the medication index prediction model 1 can include at least two medication index prediction networks (the medication index prediction network 1 to the medication index prediction network n in Figure 3, where n is a positive integer).
- the model training process of the medication index prediction model will be explained by taking at least two medication index prediction networks as medication index prediction network 1 to medication index prediction network n as examples, and will not be repeated here.
- the sample data of a user may contain at least two user attribute information (such as user attribute information X 1 to user attribute information X n ), and user attribute information X 1 to user attribute information X n may include But it is not limited to the user’s age, gender, glycosylated hemoglobin value, creatinine value and other health indicators for diabetes, and the drugs used by the user for diabetes in the above sample data are used as the above-mentioned medication index prediction network 1 to medication index prediction network n.
- user attribute information X 1 to user attribute information X n may include but it is not limited to the user’s age, gender, glycosylated hemoglobin value, creatinine value and other health indicators for diabetes, and the drugs used by the user for diabetes in the above sample data are used as the above-mentioned medication index prediction network 1 to medication index prediction network n.
- the output dimension, the drugs used by all users for diabetes can include multiple drugs (such as two drugs, drug a (such as biguanide) and drug b (such as sulfonylurea)), and at the same time use the target medication index (such as drug a)
- the target medication index Y a and the target medication index Y b under the action of the drug b are used as the output of the medication index prediction model 1 to perform model training on the medication index prediction network 1 to the medication index prediction network n.
- the target medication index here can be the expected glycation compliance rate of the target user under the action of a drug.
- the computer equipment can input the above sample data into the medication index prediction model 1.
- each user attribute information (user attribute information X 1 to user attribute information X n ) in the sample data can be compared with the medication index prediction network 1 to medication
- the index prediction network n is matched, and the matched medication index prediction network is used as the medication index prediction network to input user attribute information.
- user attribute information X 1 matches the medication index prediction network 1
- user attribute information X 2 matches the medication index prediction
- the network 2 matches, ..., the medication index prediction network n matches the user attribute information X n .
- the computer device when the computer device inputs the above sample data into the medication index prediction model 1, it will input user attribute information X 1 into its matching medication index prediction network 1, and user attribute information X 2 into its matching medication index prediction network 2...., input user attribute information X n into its matched medication index prediction network n.
- the computer equipment can perform joint learning of user attribute information X 1 to user attribute information X n through the medication index prediction network 1 to the medication index prediction network n, so as to obtain the predicted user attribute information of any user (such as the target user’s Target user attribute information X 1 to target user attribute information X n ) the medication index under the action of drug a (such as f 1a (X 1 ), f 2a (X 2 ),..., f na (X n )) and the drug The ability of medication indicators (such as f 1b (X 1 ), f 2b (X 2 )..., f nb (X n )) under the action of b.
- drug a such as f 1a (X 1 ), f 2a (X 2 ),..., f na (X n )
- drug a such as f 1a (X 1 ), f 2a (X 2 ),..., f na (X n )
- drug a such
- f 1a (X 1 ) is the medication index of any user's user attribute information X 1 (such as target user attribute information X 1 ) under the action of drug a
- f 1b (X 1 ) is the user attribute information X 1 in the drug b Medication index under the action.
- f 2a (X 2 ) is the medication index of any user's user attribute information X 2 (such as target user attribute information X 2 ) under the action of drug a
- f 2b (X 2 ) is the user attribute information X 2 acting on the drug b Medication indicators under.
- f na (X n ) is the user attribute information X n of any user (such as target user attribute information X n ) the medication index under the action of drug a
- f nb (X n ) is the user attribute information X n Medication index under the action of drug b.
- the medication index here may be the expected glycation compliance rate of a user attribute information of any user under the action of a drug.
- the computer equipment passes the above f 1a (X 1 ), f 2a (X 2 ) , ..., f na (X n ) and a corresponding offset parameter medicament beta] is accumulated to obtain a Y a, and through said f 1b (X 1), f 2b (X 2), ..., f nb (X n) and a pharmaceutically offset parameter b corresponding beta] b are accumulated to obtain Y b.
- the computer device can use the binary cross-entropy loss function to pair Y a (or Y b ) and the label parameters corresponding to the target sample medication index in the above sample data (for example, the glycosylated hemoglobin value reaches the standard of 1, The glycosylated hemoglobin value is not up to the standard and is 0) to calculate the loss value corresponding to the sample data.
- the computer device can iteratively update the bias parameters corresponding to each drug (such as the bias parameter ⁇ a corresponding to drug a and the bias parameter ⁇ b corresponding to drug b ) and the medication index prediction network according to the loss values corresponding to all sample data 1 to the medication index predicts each network parameter corresponding to the network n.
- the medication index prediction network 1 to the medication index prediction network n can predict the medication index of any user's user attribute information under the action of medication a and medication b based on the iteratively updated network parameters (such as the above f 1a ( X 1 ), f 2a (X 2 ), ..., f na (X n ), and f 1b (X 1 ), f 2b (X 2 ), ..., f nb (X n )).
- the medication index prediction model 1 can also predict any user (such as the target user) in each drug (such as the above-mentioned drug a and drug b) based on the iteratively updated bias parameter ⁇ a and the bias parameter ⁇ b and each network parameter.
- Each target medication index under the action such as the above Y a and Y b ).
- the computer device can obtain at least two types of target user attribute information of the target user, where at least two types of target user attribute information (such as target user attribute information X 1 to target user attribute information X n ) include One or more of the age, gender, and health indicators of the target disease (such as the above-mentioned glycosylated hemoglobin value and creatinine value) of the target user.
- at least two types of target user attribute information such as target user attribute information X 1 to target user attribute information X n
- the target user attribute information include One or more of the age, gender, and health indicators of the target disease (such as the above-mentioned glycosylated hemoglobin value and creatinine value) of the target user.
- the computer device can input at least two types of target user attribute information into the above-mentioned medication index prediction model (such as the above-mentioned medication index prediction model 1), and the attribute information of each target user can be respectively compared with
- the medication index prediction network 1 matches the medication index prediction network n
- the matched medication index prediction network is used as the medication index prediction network for inputting the attribute information of the target user.
- the target user attribute information X 1 matches the medication index prediction network 1
- the target user attribute information X 2 matches the medication index prediction network 2
- the target medication index prediction network n matches the user attribute information X n .
- the computer device can input the target user attribute information X 1 to the medication index prediction network 1, and the target user attribute information X 2 Input to the medication index prediction network 2,..., input the target user attribute information X n to the medication index prediction network n, and further determine the target user attribute information of each target user through the medication index prediction network 1 to the medication index prediction network n Medication index under the action of each drug.
- Step S102 based on the medication index prediction network in the medication index prediction model, determine the medication index of each target user attribute information of the target user under the action of each drug.
- each medication index prediction network is the above medication index prediction network 1 to medication index prediction network n
- the computer equipment can determine the target user attribute information X 1 medication under the action of drug a through the above medication index prediction network 1 indicator f 1a (X 1) and medication index f 1b (X 1) under b action of drugs, by the above treatment predictor network 2 to determine the target user target user attribute information X 2 medication index at the drug a function f 2a ( X 2 ) and the medication index f 2b (X 2 ) under the action of the drug b.
- the computer device determines the target user via the network administration predictor n X n target user attribute information in the drug action of a drug f index Na (X n) and f indicators medication effects of drugs under b Nb (X n ), where a kind of user attribute information corresponds to a medication index under the action of a kind of medicine.
- Step S103 Determine the target drug use indicators corresponding to at least two target user attribute information under the action of any drug based on the drug use index prediction model to obtain multiple target drug use indicators corresponding to multiple drugs.
- the model parameters corresponding to the medication index prediction model may include the bias parameters corresponding to each drug (such as the above-mentioned drug a and the above-mentioned drug b) (such as the above-mentioned bias parameter ⁇ a and the bias parameter ⁇ b )
- a drug corresponds to a bias parameter (for example, the above-mentioned drug a corresponds to the bias parameter ⁇ a and the drug b corresponds to the bias parameter ⁇ b ).
- the computer equipment can accumulate the medication index of each target user attribute information under the action of any drug and the bias parameter corresponding to any drug based on the drug index prediction model to obtain at least two target user attribute information corresponding to any drug action Target medication index. Assuming that the target medication index is the target medication index Y a of the target user under the action of the above-mentioned drug a, the following formula (1) can be used to determine Y a :
- f 1a (X 1 ) can represent the target user's target user attribute information X 1 medication index under the action of drug a
- f 2a (X 2 ) can represent the target user's target user attribute information X 2 under the action of drug a
- the medication index of ,..., f na (X n ) can represent the medication index of the target user attribute information X n of the target user under the action of the drug a
- ⁇ a can represent the bias parameter corresponding to the drug a.
- the target medication index is the target medication index Y b of the target user under the action of the above-mentioned medication b
- the following formula (2) can be used to determine Y b :
- f 1b (X 1 ) can represent the target user's target user attribute information X 1 medication index under the action of drug b
- f 2b (X 2 ) can represent the target user's target user attribute information X 2 under the action of drug b
- the medication index of,..., f nb (X n ) can represent the medication index of the target user attribute information X n of the target user under the action of the drug b
- ⁇ b can represent the bias parameter corresponding to the drug b.
- the computer device can output multiple target medication indicators corresponding to the target user under the action of multiple drugs through the medication indicator prediction model (for example, the target medication index Y a under the action of drug a or the target medication indicator Y under the action of drug b. b ), where the target user corresponds to a target medication index under the action of a drug.
- the medication indicator prediction model for example, the target medication index Y a under the action of drug a or the target medication indicator Y under the action of drug b. b
- the target user corresponds to a target medication index under the action of a drug.
- Step S104 Determine the maximum target medication index from the multiple target medication indicators, and push the target drug with the largest target medication index to the target user.
- the computer device can sort multiple target medication indicators output by the medication indicator prediction model (for example, sort from large to small or from small to large) to obtain a sequence of target medication indicators, and compare the target medication indicators The first or last target medication index in the sequence is used as the maximum target medication index.
- the computer device can push the target drug with the largest target medication index to the user interface of the target user terminal (such as the user interface of the above-mentioned CDSS or the user interface of the self-service machine), and the target user can view the target on the user interface. drug.
- the multiple drugs include the target drug and at least one other drug other than the target drug
- the medication index of the target user attribute information under the action of the target drug is the first medication index
- the target user attribute information is in other drugs.
- the medication index under the action of the drug is the second medication index.
- the computer device can determine the optimized medication index based on the first medication index and the second medication index, and display the optimized medication index to the user interface of the target user in a visual form (for example, a graph and/or table), so that the target user can easily access the user interface. View optimized medication indicators on the interface.
- the optimized medication index may be the medication index difference between the first medication index and the second medication index.
- the optimized medication index is used to instruct the target user to use the target drug with respect to the use of the second drug under a kind of target user attribute information.
- the advantages It can be seen that the computer equipment can display the optimized medication index to the target user in a visual form, and the target user can intuitively see the reason for recommending the target drug, thereby improving the interpretability of the model and improving the safety of medication recommendation Be sticky with users.
- the computer device can determine the medication index of each target user attribute information of each target user under the action of each drug through the medication index prediction network in the medication index prediction model, and then determine the effect of any drug based on the medication index prediction model
- the medication index prediction network in the medication index prediction model
- the target medication indicator is further determined according to each medication indicator, thereby improving the accuracy of the model predicting the target medication indicator.
- the computer device can determine multiple target medication indicators corresponding to multiple drugs based on the medication indicator prediction model, and push the target drug with the largest target medication indicator to the target user, thereby accurately recommending medication to the user and improving
- the accuracy of model drug recommendation is improved, while the user stickiness of drug recommendation is improved, and the applicability is strong.
- the medication recommendation device may be a computer program (including program code) running in a computer device.
- the medication recommendation device is an application software; the medication recommendation device may be used to perform corresponding steps in the method provided in this application.
- the medication recommendation device 1 may include: a second acquisition module 10, a third acquisition module 20, a first acquisition module 30, a first determination module 40, a second determination module 50, a push module 60, and a display module 70.
- the first obtaining module 30 is configured to obtain at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into the medication index prediction model.
- the medication index prediction model includes at least two medication index prediction networks, one medication The index prediction network is used to output multiple medication indicators of a user attribute information of the user under the action of multiple drugs;
- the first determining module 40 is used to determine the medication index of each target user attribute information of the target user under the action of each drug based on the drug usage index prediction network in the medication index prediction model, and one type of target user attribute information is under the action of a drug Corresponding to a medication index;
- the second determining module 50 is configured to determine the target drug use indicators corresponding to at least two target user attribute information under the action of any drug based on the drug use indicator prediction model to obtain multiple target drug indicators corresponding to multiple drugs;
- the pushing module 60 is used to determine the maximum target medication index from a plurality of target medication indicators, and push the target drug with the largest target medication index to the target user.
- the at least two types of target user attribute information include at least one of the target user's age, gender, and health indicators for the target disease.
- the multiple drugs include the target drug and at least one other drug other than the target drug, the medication index of the target user attribute information under the action of the target drug is the first medication index, and the target user attribute information is in other drugs.
- the medication index under the action of the drug is the second medication index;
- the above-mentioned drug recommendation device 1 further includes:
- the display module 70 is configured to determine an optimized medication index based on the first medication index and the second medication index, and display the optimized medication index to the target user.
- the above-mentioned drug recommendation device 1 further includes:
- the second acquisition module 10 is configured to acquire sample data of at least two users, and the sample data of one user includes at least two user attribute information of the user and the user's target sample medication index under the action of the sample drug;
- the third acquisition module 20 is used to input the sample data of at least two users into the medication index prediction model, and perform joint learning on the user attribute information corresponding to each medication index prediction network through each medication index prediction network in the medication index prediction model to obtain The ability to predict the medication index of each user attribute information of any user under the action of each drug.
- the medication index prediction model includes bias parameters corresponding to each drug, and one drug corresponds to one bias parameter;
- the second determining module 50 includes: an accumulating unit 501.
- the accumulation unit 501 is used to accumulate the medication index of each target user attribute information under the action of any drug and the bias parameter corresponding to any drug based on the medication index prediction model to obtain at least two target user attributes under the action of any drug The target medication index corresponding to the information.
- step S103 For the specific implementation of the accumulating unit 501, reference may be made to the description of step S103 in the embodiment corresponding to FIG. 2, which will not be repeated here.
- the specific implementation of the second acquisition module 10, the third acquisition module 20, the first acquisition module 30, the first determination module 40, the second determination module 50, the push module 60, and the display module 70 can be found in the above-mentioned FIG. 2 Corresponding to the description of step S101 to step S104 in the embodiment, the description will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated.
- FIG. 5 is a schematic diagram of the structure of the computer device provided by the present application.
- the computer device 1000 may be the server 10 in the embodiment corresponding to FIG. 1.
- the computer device 1000 may include: at least one processor 1001, such as a CPU, at least one network interface 1004, a user interface 1003, and a memory 1005, at least one communication bus 1002.
- the communication bus 1002 is used to implement connection and communication between these components.
- the user interface 1003 may include a display (display) and a keyboard (keyboard), and the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).
- the memory 1005 may be a high-speed random access memory (RAM) memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory.
- the memory 1005 may also be at least one storage device located far away from the aforementioned processor 1001.
- the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a device control application program.
- the network interface 1004 is mainly used for network communication with a user terminal; and the user interface 1003 is mainly used to provide an input interface for the user; and the processor 1001 can be used to call the storage in the memory 1005
- the device control application to achieve:
- the medication index prediction model includes at least two medication index prediction networks, and one medication index prediction network is used to output the user’s information Multiple medication indicators of a user attribute information under the action of multiple drugs;
- the computer device 1000 described in this application can execute the description of the drug recommendation method in the foregoing embodiment corresponding to FIG. 2 and the description of the drug recommendation device 1 in the foregoing embodiment corresponding to FIG. 4, I won't repeat them here. In addition, the description of the beneficial effects of using the same method will not be repeated.
- this application also provides a computer-readable storage medium, and the computer-readable storage medium stores the aforementioned computer program executed by the drug recommendation device 1, and the computer program includes Program instructions.
- the processor executes the program instructions, it can execute the description of the drug recommendation method in the embodiment corresponding to FIG. 2 above, and therefore, it will not be repeated here.
- the description of the beneficial effects of using the same method will not be repeated.
- the program instructions may be deployed to be executed on one computing device, or on multiple computing devices located in one location, or on multiple computing devices that are distributed in multiple locations and interconnected by a communication network
- multiple computing devices distributed in multiple locations and interconnected through a communication network can form a blockchain system.
- the storage medium involved in this application such as a computer-readable storage medium, may be non-volatile or volatile.
- the above-mentioned program can be stored in a computer readable storage medium. When executed, it may include the procedures of the above-mentioned method embodiments.
- the foregoing computer-readable storage medium may be the medication recommendation device provided in any of the foregoing embodiments or the internal storage unit of the foregoing device, such as a hard disk or memory of an electronic device.
- the computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), or a secure digital (SD) card equipped on the electronic device. Flash card, etc.
- the above-mentioned computer-readable storage medium may also include a magnetic disk, an optical disk, a read-only memory (read-only memory, ROM), or RAM. Further, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.
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Abstract
Provided are a drug recommendation method, apparatus, computer device, and storage medium; the method is suitable for use in the field of digital medicine, and comprises: obtaining at least two types of target user attribute information of a target user, and inputting the at least two types of target user attribute information into a medication index prediction model (S101); on the basis of a medication index prediction network in the medication index prediction model, determining the medication index of each target user attribute information of the target user under the action of each drug (S102); on the basis of the medication index prediction model, determining a target medication index corresponding to at least two target user attribute information under the action of any drug so as to obtain a plurality of target medication indicators corresponding to a plurality of drugs (S103); determining a maximum target medication index from among the plurality of target medication indexes, and pushing to the target user the target drug having the largest target medication index (S104). Using the method, it is possible to accurately recommend drugs to a user, improving the accuracy of model drug recommendations, and also increasing the user stickiness of drug recommendations.
Description
本申请要求于2020年10月9日提交中国专利局、申请号为202011070302.8,发明名称为“药物推荐方法、装置、计算机设备及存储介质”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on October 9, 2020, the application number is 202011070302.8, and the invention title is "method, device, computer equipment and storage medium for drug recommendation", the entire content of which is incorporated by reference In this application.
本申请涉及计算机技术领域,尤其涉及一种药物推荐方法、装置、计算机设备及存储介质。This application relates to the field of computer technology, and in particular to a method, device, computer equipment and storage medium for drug recommendation.
目前,可以通过神经网络模型或者统计模型对患者推荐药物。发明人意识到,在通过神经网络模型对患者推荐药物时,神经网络模型可以根据患者的所有特征预测需要向该患者推荐的药物,但是由于神经网络共享神经元的特性,只能对患者的所有特征进行解释,而不能对患者的某个特征进行解释,导致了患者可能不会接受上述模型所推荐的药物,由此可见,上述神经网络模型缺乏足够的可解释性,适用性差。另外,在通过统计模型对患者推荐药物时,虽然统计模型具有足够的可解释性,但模型精确度低。Currently, drugs can be recommended to patients through neural network models or statistical models. The inventor realized that when recommending drugs to a patient through a neural network model, the neural network model can predict the drugs that need to be recommended to the patient based on all the characteristics of the patient, but because the neural network shares the characteristics of neurons, it can only The characteristics are explained, but a certain characteristic of the patient cannot be explained. As a result, the patient may not accept the drugs recommended by the above-mentioned model. It can be seen that the above-mentioned neural network model lacks sufficient interpretability and poor applicability. In addition, when recommending drugs to patients through a statistical model, although the statistical model has sufficient interpretability, the accuracy of the model is low.
发明内容Summary of the invention
本申请提供一种药物推荐方法、装置、计算机设备及存储介质,可以对用户精准推荐药物,提高了模型药物推荐的精确度,也提高了药物推荐的用户粘性。This application provides a drug recommendation method, device, computer device, and storage medium, which can accurately recommend drugs to users, improve the accuracy of model drug recommendation, and also increase the user stickiness of drug recommendation.
第一方面,本申请提供了一种药物推荐方法,该方法包括:In the first aspect, this application provides a drug recommendation method, which includes:
获取目标用户的至少两种目标用户属性信息,将至少两种目标用户属性信息输入用药指标预测模型,用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;Obtain at least two target user attribute information of the target user, and input the at least two target user attribute information into the medication index prediction model. The medication index prediction model includes at least two medication index prediction networks, and one medication index prediction network is used to output the user’s information Multiple medication indicators of a user attribute information under the action of multiple drugs;
基于用药指标预测模型中的各用药指标预测网络确定目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;Based on the medication index prediction network in the medication index prediction model, determine the medication index of each target user attribute information of the target user under the action of each drug, and one type of target user attribute information corresponds to a medication index under the action of a drug;
基于用药指标预测模型确定出任一药物作用下至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;Determine the target drug use indicators corresponding to at least two target user attribute information under the action of any drug based on the drug use index prediction model to obtain multiple target drug use indicators corresponding to multiple drugs;
从多个目标用药指标中确定出最大目标用药指标,并将具有最大目标用药指标的目标药物推送给目标用户。Determine the maximum target medication index from multiple target medication indicators, and push the target drug with the largest target medication index to the target user.
第二方面,本申请提供了一种药物推荐装置,该装置包括:In the second aspect, the present application provides a drug recommendation device, which includes:
第一获取模块,用于获取目标用户的至少两种目标用户属性信息,将至少两种目标用户属性信息输入用药指标预测模型,用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;The first acquisition module is used to acquire at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into the medication index prediction model. The medication index prediction model includes at least two medication index prediction networks, one medication index The prediction network is used to output multiple medication indicators of a user attribute information of the user under the action of multiple drugs;
第一确定模块,用于基于用药指标预测模型中的各用药指标预测网络确定目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;The first determination module is used to determine the medication index of each target user attribute information of the target user under the action of each drug based on the drug index prediction network in the medication index prediction model, and one type of target user attribute information corresponds to the action of a drug A medication index;
第二确定模块,用于基于用药指标预测模型确定出任一药物作用下至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;The second determining module is configured to determine the target drug use indicators corresponding to at least two target user attribute information under any drug action based on the drug use indicator prediction model to obtain multiple target drug indicators corresponding to multiple drugs;
推送模块,用于从多个目标用药指标中确定出最大目标用药指标,并将具有最大目标用药指标的目标药物推送给目标用户。The push module is used to determine the maximum target medication indicator from multiple target medication indicators, and push the target drug with the largest target medication indicator to the target user.
第三方面,本申请提供了一种计算机设备,包括:处理器、存储器、网络接口;In the third aspect, this application provides a computer device, including: a processor, a memory, and a network interface;
该处理器与存储器、网络接口相连,其中,网络接口用于提供数据通信功能,该存储器用于存储计算机程序,该处理器用于调用该计算机程序,执行以下方法:The processor is connected to a memory and a network interface, where the network interface is used to provide data communication functions, the memory is used to store a computer program, and the processor is used to call the computer program to execute the following methods:
获取目标用户的至少两种目标用户属性信息,将至少两种目标用户属性信息输入用药指标预测模型,用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测 网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;Obtain at least two target user attribute information of the target user, and input the at least two target user attribute information into the medication index prediction model. The medication index prediction model includes at least two medication index prediction networks, and one medication index prediction network is used to output the user’s information Multiple medication indicators of a user attribute information under the action of multiple drugs;
基于用药指标预测模型中的各用药指标预测网络确定目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;Based on the medication index prediction network in the medication index prediction model, determine the medication index of each target user attribute information of the target user under the action of each drug, and one type of target user attribute information corresponds to a medication index under the action of a drug;
基于用药指标预测模型确定出任一药物作用下至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;Determine the target drug use indicators corresponding to at least two target user attribute information under the action of any drug based on the drug use index prediction model to obtain multiple target drug use indicators corresponding to multiple drugs;
从多个目标用药指标中确定出最大目标用药指标,并将具有最大目标用药指标的目标药物推送给目标用户。Determine the maximum target medication index from multiple target medication indicators, and push the target drug with the largest target medication index to the target user.
第四方面,本申请提供了一种计算机可读存储介质,该计算机可读存储介质存储有计算机程序,该计算机程序包括程序指令,该程序指令当被处理器执行时,执行以下方法:In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when executed by a processor, the program instructions execute the following methods:
获取目标用户的至少两种目标用户属性信息,将至少两种目标用户属性信息输入用药指标预测模型,用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;Obtain at least two target user attribute information of the target user, and input the at least two target user attribute information into the medication index prediction model. The medication index prediction model includes at least two medication index prediction networks, and one medication index prediction network is used to output the user’s information Multiple medication indicators of a user attribute information under the action of multiple drugs;
基于用药指标预测模型中的各用药指标预测网络确定目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;Based on the medication index prediction network in the medication index prediction model, determine the medication index of each target user attribute information of the target user under the action of each drug, and one type of target user attribute information corresponds to a medication index under the action of a drug;
基于用药指标预测模型确定出任一药物作用下至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;Determine the target drug use indicators corresponding to at least two target user attribute information under the action of any drug based on the drug use index prediction model to obtain multiple target drug use indicators corresponding to multiple drugs;
从多个目标用药指标中确定出最大目标用药指标,并将具有最大目标用药指标的目标药物推送给目标用户。Determine the maximum target medication index from multiple target medication indicators, and push the target drug with the largest target medication index to the target user.
本申请通过各用药指标预测网络确定出各用药指标,进一步根据各用药指标确定目标用药指标,从而提高了模型预测目标用药指标的精确度。进一步地,计算机设备可以基于用药指标预测模型确定出多种药物作用下对应的多个目标用药指标,并将具有最大目标用药指标的目标药物推送给目标用户,从而可以向用户精准推荐用药,提高了模型药物推荐的精确度,同时也提高了药物推荐的用户粘性,适用性强。In this application, each medication index is determined through the medication index prediction network, and the target medication index is further determined according to each medication index, thereby improving the accuracy of the model for predicting the target medication index. Further, the computer device can determine multiple target medication indicators corresponding to multiple drugs based on the medication indicator prediction model, and push the target drug with the largest target medication indicator to the target user, thereby accurately recommending medication to the user and improving The accuracy of model drug recommendation is improved, and the user stickiness of drug recommendation is also improved, and the applicability is strong.
为了更清楚地说明本申请中的技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本申请的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to explain the technical solutions in this application more clearly, the following will briefly introduce the drawings needed in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the application. For those of ordinary skill in the art, without creative work, other drawings can be obtained from these drawings.
图1是本申请提供的网络架构的结构示意图;Figure 1 is a schematic structural diagram of the network architecture provided by this application;
图2是本申请提供的药物推荐方法的流程示意图;Figure 2 is a schematic flow chart of the drug recommendation method provided by the present application;
图3是本申请提供的用药指标预测模型的结构示意图;Figure 3 is a schematic diagram of the structure of the medication index prediction model provided by this application;
图4是本申请提供的药物推荐装置的结构示意图;4 is a schematic diagram of the structure of the drug recommendation device provided by the present application;
图5是本申请提供的计算机设备的结构示意图。Fig. 5 is a schematic diagram of the structure of the computer equipment provided by the present application.
下面将结合本申请中的附图,对本申请中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.
本申请的技术方案可应用于人工智能、智慧城市、数字医疗、区块链和/或大数据技术领域,以实现智能化药物推荐。可选的,本申请涉及的数据如用户属性信息、用药指标和/或推送的药物信息等可存储于数据库中,或者可以存储于区块链中,比如通过区块链分布式存储,本申请不做限定。The technical solution of this application can be applied to the fields of artificial intelligence, smart city, digital medical, blockchain and/or big data technology to realize intelligent drug recommendation. Optionally, the data involved in this application, such as user attribute information, medication indicators, and/or pushed medication information, can be stored in a database, or can be stored in a blockchain, such as distributed storage through a blockchain, this application Not limited.
请参见图1,图1是本申请提供的网络架构的结构示意图。如图1所示,该网络架构可以包括服务器10和用户终端集群,该用户终端集群可以包括多个用户终端,如图1所示,具体可以包括用户终端100a、用户终端100b、用户终端100c、…、用户终端100n。Please refer to Fig. 1, which is a schematic structural diagram of the network architecture provided by the present application. As shown in FIG. 1, the network architecture may include a server 10 and a user terminal cluster. The user terminal cluster may include multiple user terminals. As shown in FIG. 1, it may specifically include a user terminal 100a, a user terminal 100b, and a user terminal 100c. ..., the user terminal 100n.
其中,服务器10可以为独立的物理服务器,也可以是提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、内容分发网络(content delivery network,CDN)、大数据以及人工智能平台等基础云计算服务的云服务器。用户终端集群中的每个用户终端均可以包括但不限于:智能手机、平板电脑、笔记本电脑、台式计算机、智能音箱、智能手表等智能终端。Among them, the server 10 may be an independent physical server, or it may provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks ( content delivery network (CDN), big data and artificial intelligence platform and other basic cloud computing services cloud server. Each user terminal in the user terminal cluster may include, but is not limited to: smart phones, tablet computers, notebook computers, desktop computers, smart speakers, smart watches, and other smart terminals.
可以理解的是,本申请中的计算机设备可以为具有药物推荐功能的实体终端,该实体终端可以为如图1所示的服务器10,也可以为用户终端,在此不做限定。It is understandable that the computer device in the present application may be a physical terminal with a drug recommendation function. The physical terminal may be the server 10 as shown in FIG. 1 or a user terminal, which is not limited here.
如图1所示,用户终端100a、用户终端100b、用户终端100c、…、用户终端100n可以分别与上述服务器10进行网络连接,以便于每个用户终端可以通过该网络连接与服务器10进行数据交互。例如,服务器10可以将目标药物推送给目标用户的用户终端(或简称目标用户终端)的用户界面,这时目标用户可以在该用户界面上查看该目标药物,其中,目标用户终端可以为用户终端集群中的任意一个用户终端(如用户终端100a)。本申请可以将基于用药指标预测模型确定的用于向目标用户推荐的药物称之为目标药物,本申请还可以将具有预测目标用户在多种药物作用下的目标用药指标的功能的模型称之为用药指标预测模型。As shown in FIG. 1, the user terminal 100a, the user terminal 100b, the user terminal 100c, ..., the user terminal 100n can be connected to the aforementioned server 10 respectively, so that each user terminal can interact with the server 10 through the network connection. . For example, the server 10 may push the target drug to the user interface of the target user's user terminal (or target user terminal for short), and then the target user can view the target drug on the user interface, where the target user terminal may be a user terminal Any user terminal in the cluster (such as the user terminal 100a). The application may refer to the drug recommended to the target user determined based on the drug index prediction model as the target drug, and the application may also call the model with the function of predicting the target drug index of the target user under the action of multiple drugs as the target drug. It is a predictive model of medication index.
本申请提供的药物推荐方法可适用于针对任一疾病的药物推荐场景,比如糖尿病药物推荐场景、高血压药物推荐场景或者其它疾病的药物推荐场景。假设目标用户为医生,医生可以将患者的基本信息输入至临床决策支持***(clinical decision support system,CDSS),且CDSS中包含上述用药指标预测模型,可以基于患者的基本信息将推荐的目标药物输出至CDSS的用户界面,这时医生可以在该用户界面上查看该目标药物(这里的目标药物可以作为初步诊断结果),再结合自己对患者的进一步诊断结果确定适合该患者的药物(如上述目标药物)。假设目标用户为患者,患者可以将自己的基本信息输入至医院、卫生站或者社康等医疗机构提供的自助终端(或简称自助机等),该自助机中包含上述用药指标预测模型,可以基于患者的基本信息将推荐的目标药物输出至该自助机的用户界面。患者可以在该自助机的用户界面中查看该目标药物,后续患者可以直接购买该目标药物,也可以让医生进一步诊断确定适合该患者的药物(如上述目标药物)。The drug recommendation method provided in this application may be applicable to a drug recommendation scenario for any disease, such as a diabetes drug recommendation scenario, a hypertension drug recommendation scenario, or a drug recommendation scenario for other diseases. Assuming that the target user is a doctor, the doctor can input the basic information of the patient into the clinical decision support system (CDSS), and the CDSS contains the above-mentioned medication index prediction model, which can output the recommended target drug based on the basic information of the patient To the user interface of the CDSS, the doctor can view the target drug on the user interface (the target drug here can be used as the preliminary diagnosis result), and then combine with the patient's further diagnosis results to determine the drug suitable for the patient (such as the above target drug). Assuming that the target user is a patient, the patient can input their basic information into self-service terminals (or self-service kiosks for short) provided by hospitals, health stations, or social health institutions. The self-service kiosks contain the above-mentioned medication index prediction model, which can be based on The basic information of the patient outputs the recommended target medicine to the user interface of the self-service machine. The patient can view the target drug in the user interface of the self-service machine, and the subsequent patient can directly purchase the target drug, or allow the doctor to further diagnose and determine the drug suitable for the patient (such as the above target drug).
为方便描述,下面将以糖尿病药物推荐场景为例进行说明,以下不再赘述。下面将结合图2至图5对本申请的药物推荐方法、药物推荐装置以及计算机设备进行说明。For the convenience of description, the following will take the diabetic drug recommendation scenario as an example for description, which will not be repeated below. The drug recommendation method, drug recommendation device, and computer equipment of the present application will be described below with reference to FIGS. 2 to 5.
请参见图2,图2是本申请提供的药物推荐方法的流程示意图。如图2所示,该方法可以包括以下步骤S101-步骤S104:Please refer to FIG. 2, which is a schematic flowchart of the drug recommendation method provided by the present application. As shown in Figure 2, the method may include the following steps S101 to S104:
步骤S101,获取目标用户的至少两种目标用户属性信息,将至少两种目标用户属性信息输入用药指标预测模型。Step S101: Obtain at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into the medication index prediction model.
可以理解,在执行步骤S101之前,计算机设备可以先通过至少两个用户的样本数据对用药指标预测模型(如神经加性模型(neural additive models,NAM))中的各用药指标预测网络进行训练,从而得到用于输出任一用户的各用户属性信息在多种药物作用下的用药指标的各用药指标预测网络。其中,用药指标预测模型中用药指标预测网络的个数与用户的用户属性信息的个数相同,假设用药指标预测模型对应一个神经网络,该神经网络中可以包含多个子神经网络,该用药指标预测网络可以为子神经网络。It can be understood that, before performing step S101, the computer device may first train the medication index prediction network in the medication index prediction model (such as neural additive models (NAM)) through the sample data of at least two users. Thus, each medication index prediction network used to output the medication index of each user attribute information of any user under the action of multiple drugs is obtained. Among them, the number of medication index prediction networks in the medication index prediction model is the same as the number of user attribute information of the user. It is assumed that the medication index prediction model corresponds to a neural network. The neural network can contain multiple sub-neural networks. The medication index prediction The network can be a sub-neural network.
在一些可行的实施方式中,计算机设备可以获取至少两个用户的样本数据。其中,至少两个用户的样本数据用于训练用药指标预测模型,一个用户对应一个样本数据,一个样本数据中可以包括至少两种用户属性信息和用户在药物作用下的目标样本用药指标,这里的目标样本用药指标可以为用户在药物作用下的实际用药指标。不同用户针对目标疾病使用的药物可以相同,也可以不同,具体可根据实际应用场景确定,在此不作限制。进一步地,计算机设备可以将至少两个用户的样本数据输入用药指标预测模型,通过用药指标预 测模型中的各用药指标预测网络对各用药指标预测网络对应的用户属性信息进行联合学习以获取预测任一用户的各用户属性信息在各药物作用下的用药指标的能力。In some feasible implementation manners, the computer device can obtain sample data of at least two users. Among them, the sample data of at least two users is used to train the medication index prediction model, one user corresponds to one sample data, and one sample data can include at least two types of user attribute information and the user's target sample medication index under the action of drugs. The target sample medication index may be the actual medication index of the user under the action of the drug. The drugs used by different users for the target disease can be the same or different, and the specific drugs can be determined according to the actual application scenario, which is not limited here. Further, the computer device can input the sample data of at least two users into the medication index prediction model, and perform joint learning on the user attribute information corresponding to each medication index prediction network through each medication index prediction network in the medication index prediction model to obtain the prediction task. The ability of the user's attribute information of each user to use the medication index under the action of each drug.
可以理解,计算机设备在获取到至少两个用户的样本数据之后,对至少两个用户的样本数据进行数据清洗、特征筛选,最终形成关于预测任一用户的各用户属性信息在各药物作用下的用药指标的一些特征数据。这里的特征数据可以包括但不限于用户的年龄、性别以及针对目标疾病(如糖尿病)的健康指标(如糖化血红蛋白值和肌酐值)等多个维度的特征数据,在此不做限制。进一步地,计算机设备可以将至少两个用户的样本数据输入用药指标预测模型,通过用药指标预测模型中的各用药指标预测网络,以预测任一用户的各用户属性信息在各药物作用下的用药指标为学习任务,对各用药指标预测网络对应的用户属性信息进行联合学习,以获得各用药指标预测网络对应的各网络参数并基于各网络参数确定出任一用户的各用户属性信息在各药物作用下的用药指标的能力。换句话说,计算机设备可以基于各用药指标预测网络对上述特征数据进行数据特征学习以获取预测任一用户的各用户属性信息在各药物作用下的用药指标的能力。It can be understood that after obtaining the sample data of at least two users, the computer equipment performs data cleaning and feature screening on the sample data of at least two users, and finally forms a prediction about the user attribute information of any user under the action of each drug. Some characteristic data of medication indicators. The feature data here may include, but is not limited to, the user's age, gender, and health indicators for target diseases (such as diabetes) (such as glycosylated hemoglobin value and creatinine value) and other multiple dimension feature data, which is not limited here. Further, the computer device can input the sample data of at least two users into the medication index prediction model, and use the medication index prediction network in the medication index prediction model to predict the medication of each user attribute information of any user under the action of each drug. The index is a learning task. The user attribute information corresponding to each medication index prediction network is jointly learned to obtain each network parameter corresponding to each medication index prediction network, and based on each network parameter, determine the role of each user attribute information of any user in each drug The ability to follow the medication index. In other words, the computer device can perform data feature learning on the above-mentioned feature data based on each medication index prediction network to obtain the ability to predict the medication index of each user attribute information of any user under the action of each drug.
在用药指标预测模型中的各用药指标预测网络的联合学习过程中,计算机设备可以通过损失函数(如二元交叉熵损失函数)计算每个样本数据对应的损失值。进一步地,计算机设备可以根据每个样本数据对应的损失值迭代更新各用药指标预测网络对应的各网络参数,在损失值基本不变时停止训练,并将迭代更新后的各网络参数作为各用药指标预测网络最终的网络参数。这时也表明了各用药指标预测网络具有预测任一用户的各用户属性信息在各药物作用下的用药指标的能力,同时,用药指标预测模型具有预测任一用户(如目标用户)在各药物作用下的目标用药指标的能力。In the joint learning process of the medication index prediction network in the medication index prediction model, the computer device can calculate the loss value corresponding to each sample data through a loss function (such as a binary cross-entropy loss function). Further, the computer equipment can iteratively update the network parameters corresponding to each medication index prediction network according to the loss value corresponding to each sample data, stop training when the loss value is basically unchanged, and use the iteratively updated network parameters as each medication The index predicts the final network parameters of the network. At this time, it also shows that each medication index prediction network has the ability to predict the medication index of any user's user attribute information under the action of each drug. At the same time, the medication index prediction model has the ability to predict any user (such as the target user) in each drug. The ability to target medication indicators under the action.
为便于理解,请参见图3,图3是本申请提供的用药指标预测模型的结构示意图。如图3所示,用药指标预测模型1中可以包含至少两个用药指标预测网络(如图3中的用药指标预测网络1至用药指标预测网络n,n为正整数),为方便描述,下面将以至少两个用药指标预测网络为用药指标预测网络1至用药指标预测网络n为例对用药指标预测模型的模型训练过程进行说明,在此不再赘述。For ease of understanding, please refer to Figure 3, which is a schematic structural diagram of the medication index prediction model provided in this application. As shown in Figure 3, the medication index prediction model 1 can include at least two medication index prediction networks (the medication index prediction network 1 to the medication index prediction network n in Figure 3, where n is a positive integer). For ease of description, the following The model training process of the medication index prediction model will be explained by taking at least two medication index prediction networks as medication index prediction network 1 to medication index prediction network n as examples, and will not be repeated here.
假设上述目标疾病为糖尿病,一个用户的样本数据中可以包含至少两个用户属性信息(如用户属性信息X
1至用户属性信息X
n),且用户属性信息X
1至用户属性信息X
n可以包含但不限于用户的年龄、性别、糖化血红蛋白值、肌酐值以及针对糖尿病的其它健康指标,并将上述样本数据中用户针对糖尿病使用的药物分别作为上述用药指标预测网络1至用药指标预测网络n的输出维度,这里的所有用户针对糖尿病使用的药物可以包含多种药物(如两种药物,药物a(如双胍)和药物b(如磺脲)),同时以目标用药指标(如药物a作用下的目标用药指标Y
a以及药物b作用下的目标用药指标Y
b)作为用药指标预测模型1的输出对用药指标预测网络1至用药指标预测网络n进行模型训练。这里的目标用药指标可以为目标用户在一种药物作用下的预期糖化达标率。
Assuming that the above-mentioned target disease is diabetes, the sample data of a user may contain at least two user attribute information (such as user attribute information X 1 to user attribute information X n ), and user attribute information X 1 to user attribute information X n may include But it is not limited to the user’s age, gender, glycosylated hemoglobin value, creatinine value and other health indicators for diabetes, and the drugs used by the user for diabetes in the above sample data are used as the above-mentioned medication index prediction network 1 to medication index prediction network n. The output dimension, the drugs used by all users for diabetes can include multiple drugs (such as two drugs, drug a (such as biguanide) and drug b (such as sulfonylurea)), and at the same time use the target medication index (such as drug a) The target medication index Y a and the target medication index Y b under the action of the drug b are used as the output of the medication index prediction model 1 to perform model training on the medication index prediction network 1 to the medication index prediction network n. The target medication index here can be the expected glycation compliance rate of the target user under the action of a drug.
计算机设备可以将上述样本数据输入至用药指标预测模型1,这时,可以对样本数据中的各用户属性信息(用户属性信息X
1至用户属性信息X
n)分别与用药指标预测网络1至用药指标预测网络n进行匹配,并将匹配到的用药指标预测网络作为输入用户属性信息的用药指标预测网络,如用户属性信息X
1与用药指标预测网络1匹配,用户属性信息X
2与用药指标预测网络2匹配,…,用药指标预测网络n与用户属性信息X
n匹配。
The computer equipment can input the above sample data into the medication index prediction model 1. At this time, each user attribute information (user attribute information X 1 to user attribute information X n ) in the sample data can be compared with the medication index prediction network 1 to medication The index prediction network n is matched, and the matched medication index prediction network is used as the medication index prediction network to input user attribute information. For example, user attribute information X 1 matches the medication index prediction network 1, and user attribute information X 2 matches the medication index prediction The network 2 matches, ..., the medication index prediction network n matches the user attribute information X n .
可以理解,计算机设备在将上述样本数据输入至用药指标预测模型1时,会将用户属性信息X
1输入其匹配的用药指标预测网络1,将用户属性信息X
2输入其匹配的用药指标预测网络2,…,将用户属性信息X
n输入其匹配的用药指标预测网络n。进一步地,计算机设备可以通过用药指标预测网络1至用药指标预测网络n对用户属性信息X
1至用户属性信息X
n进行联合学习,以获取预测任一用户的各用户属性信息(如目标用户的目标用户属性信息X
1至目标用户属性信息X
n)在药物a作用下的用药指标(如f
1a(X
1)、f
2a(X
2)、…、f
na(X
n))以及在药物b作用下的用药指标(如f
1b(X
1)、f
2b(X
2)…、f
nb(X
n))的能力。其中,f
1a(X
1)为任一用户的用户属性信息X
1(如目标用户属性信息X
1)在药物a作用下的用药指标,f
1b(X
1)为用户属性信息X
1在药物b作用下的用药指标。f
2a(X
2)为任一用户的用户属性信息X
2(如目标用户属性信息X
2)在药物a作用下的用药指标,f
2b(X
2)为用户属性信息X
2在药物b作用下的用药指标。以此类推,f
na(X
n)为任一用户的用户属性信息X
n(如目标用户属性信息X
n)在药物a作用下的用药指标,f
nb(X
n)为用户属性信息X
n在药物b作用下的用药指标。这里的用药指标可以为任一用户的一种用户属性信息在一种药物作用下的预期糖化达标率。
It can be understood that when the computer device inputs the above sample data into the medication index prediction model 1, it will input user attribute information X 1 into its matching medication index prediction network 1, and user attribute information X 2 into its matching medication index prediction network 2...., input user attribute information X n into its matched medication index prediction network n. Further, the computer equipment can perform joint learning of user attribute information X 1 to user attribute information X n through the medication index prediction network 1 to the medication index prediction network n, so as to obtain the predicted user attribute information of any user (such as the target user’s Target user attribute information X 1 to target user attribute information X n ) the medication index under the action of drug a (such as f 1a (X 1 ), f 2a (X 2 ),..., f na (X n )) and the drug The ability of medication indicators (such as f 1b (X 1 ), f 2b (X 2 )..., f nb (X n )) under the action of b. Among them, f 1a (X 1 ) is the medication index of any user's user attribute information X 1 (such as target user attribute information X 1 ) under the action of drug a, f 1b (X 1 ) is the user attribute information X 1 in the drug b Medication index under the action. f 2a (X 2 ) is the medication index of any user's user attribute information X 2 (such as target user attribute information X 2 ) under the action of drug a, f 2b (X 2 ) is the user attribute information X 2 acting on the drug b Medication indicators under. By analogy, f na (X n ) is the user attribute information X n of any user (such as target user attribute information X n ) the medication index under the action of drug a, f nb (X n ) is the user attribute information X n Medication index under the action of drug b. The medication index here may be the expected glycation compliance rate of a user attribute information of any user under the action of a drug.
同时,计算机设备在对用药指标预测网络1至用药指标预测网络n的联合学习(这里的联合学习也可以称为联合训练)过程中,通过上述f
1a(X
1)、f
2a(X
2)、…、f
na(X
n)以及药物a对应的偏置参数β
a进行累加得到Y
a,以及通过上述f
1b(X
1)、f
2b(X
2)、…、f
nb(X
n)以及药物b对应的偏置参数β
b进行累加得到Y
b。在得到Y
a和Y
b之后,计算机设备可以采用二元交叉熵损失函数对Y
a(或者Y
b)以及上述样本数据中的目标样本用药指标对应的标签参数(如糖化血红蛋白值达标为1,糖化血红蛋白值未达标为0)进行计算,得到该样本数据对应的损失值。此时,计算机设备可以根据所有样本数据对应的损失值迭代更新各药物对应的偏置参数(如药物a对应的偏置参数β
a和药物b对应的偏置参数β
b)以及用药指标预测网络1至用药指标预测网络n对应的各网络参数。
At the same time, in the process of joint learning of the medication index prediction network 1 to the medication index prediction network n (the joint learning here may also be called joint training), the computer equipment passes the above f 1a (X 1 ), f 2a (X 2 ) , ..., f na (X n ) and a corresponding offset parameter medicament beta] is accumulated to obtain a Y a, and through said f 1b (X 1), f 2b (X 2), ..., f nb (X n) and a pharmaceutically offset parameter b corresponding beta] b are accumulated to obtain Y b. After obtaining Y a and Y b , the computer device can use the binary cross-entropy loss function to pair Y a (or Y b ) and the label parameters corresponding to the target sample medication index in the above sample data (for example, the glycosylated hemoglobin value reaches the standard of 1, The glycosylated hemoglobin value is not up to the standard and is 0) to calculate the loss value corresponding to the sample data. At this time, the computer device can iteratively update the bias parameters corresponding to each drug (such as the bias parameter β a corresponding to drug a and the bias parameter β b corresponding to drug b ) and the medication index prediction network according to the loss values corresponding to all sample data 1 to the medication index predicts each network parameter corresponding to the network n.
在上述损失值基本不变时停止联合训练,计算机设备可以确定迭代更新后的偏置参数β
a和偏置参数β
b以及各网络参数。此时,用药指标预测网络1至用药指标预测网络n可以基于迭代更新后的各网络参数预测任一用户的各用户属性信息在药物a和药物b作用下的各用药指标(如上述f
1a(X
1)、f
2a(X
2)、…、f
na(X
n)以及f
1b(X
1)、f
2b(X
2)、…、f
nb(X
n))。 同时,用药指标预测模型1也可以基于迭代更新后的偏置参数β
a和偏置参数β
b以及各网络参数预测任一用户(如目标用户)在各药物(如上述药物a和药物b)作用下的各目标用药指标(如上述Y
a和Y
b)。
When the aforementioned loss value is basically unchanged, the joint training is stopped, and the computer device can determine the iteratively updated bias parameter β a and the bias parameter β b and each network parameter. At this time, the medication index prediction network 1 to the medication index prediction network n can predict the medication index of any user's user attribute information under the action of medication a and medication b based on the iteratively updated network parameters (such as the above f 1a ( X 1 ), f 2a (X 2 ), ..., f na (X n ), and f 1b (X 1 ), f 2b (X 2 ), ..., f nb (X n )). At the same time, the medication index prediction model 1 can also predict any user (such as the target user) in each drug (such as the above-mentioned drug a and drug b) based on the iteratively updated bias parameter β a and the bias parameter β b and each network parameter. Each target medication index under the action (such as the above Y a and Y b ).
在训练得到用药指标预测模型之后,计算机设备可以获取目标用户的至少两种目标用户属性信息,其中,至少两种目标用户属性信息(如目标用户属性信息X
1至目标用户属性信息X
n)包括目标用户的年龄、性别以及针对目标疾病的健康指标(如上述糖化血红蛋白值和肌酐值)中的一种或者多种。
After the medication index prediction model is trained, the computer device can obtain at least two types of target user attribute information of the target user, where at least two types of target user attribute information (such as target user attribute information X 1 to target user attribute information X n ) include One or more of the age, gender, and health indicators of the target disease (such as the above-mentioned glycosylated hemoglobin value and creatinine value) of the target user.
在获取目标用户的至少两种目标用户属性信息之后,计算机设备可以将至少两种目标用户属性信息输入上述用药指标预测模型(如上述用药指标预测模型1),可以将各目标用户属性信息分别与用药指标预测网络1至用药指标预测网络n进行匹配,并将匹配到的用药指标预测网络作为输入目标用户属性信息的用药指标预测网络。比如,目标用户属性信息X
1与用药指标预测网络1匹配,目标用户属性信息X
2与用药指标预测网络2匹配,…,目标用药指标预测网络n与用户属性信息X
n匹配。可以理解,在将目标用户属性信息X
1至目标用户属性信息X
n输入用药指标预测模型1时,计算机设备可以将目标用户属性信息X
1输入至用药指标预测网络1,将目标用户属性信息X
2输入至用药指标预测网络2,…,将目标用户属性信息X
n输入至用药指标预测网络n,进一步可以通过用药指标预测网络1至用药指标预测网络n确定目标用户的各目标用户属性信息在各药物作用下的用药指标。
After acquiring at least two types of target user attribute information of the target user, the computer device can input at least two types of target user attribute information into the above-mentioned medication index prediction model (such as the above-mentioned medication index prediction model 1), and the attribute information of each target user can be respectively compared with The medication index prediction network 1 matches the medication index prediction network n, and the matched medication index prediction network is used as the medication index prediction network for inputting the attribute information of the target user. For example, the target user attribute information X 1 matches the medication index prediction network 1, the target user attribute information X 2 matches the medication index prediction network 2, ..., the target medication index prediction network n matches the user attribute information X n . It can be understood that when the target user attribute information X 1 to the target user attribute information X n are input into the medication index prediction model 1, the computer device can input the target user attribute information X 1 to the medication index prediction network 1, and the target user attribute information X 2 Input to the medication index prediction network 2,..., input the target user attribute information X n to the medication index prediction network n, and further determine the target user attribute information of each target user through the medication index prediction network 1 to the medication index prediction network n Medication index under the action of each drug.
步骤S102,基于用药指标预测模型中的各用药指标预测网络确定目标用户的各目标用户属性信息在各药物作用下的用药指标。Step S102, based on the medication index prediction network in the medication index prediction model, determine the medication index of each target user attribute information of the target user under the action of each drug.
可以理解,假设各用药指标预测网络为上述用药指标预测网络1至用药指标预测网络n,计算机设备可以通过上述用药指标预测网络1确定目标用户的目标用户属性信息X
1在药物a作用下的用药指标f
1a(X
1)和药物b作用下的用药指标f
1b(X
1),通过上述用药指标预测网络2确定目标用户的目标用户属性信息X
2在药物a作用下的用药指标f
2a(X
2)和药物b作用下的用药指标f
2b(X
2)。以此类推,计算机设备通过上述用药指标预测网络n确定目标用户的目标用户属性信息X
n在药物a作用下的用药指标f
na(X
n)和药物b作用下的用药指标f
nb(X
n),其中,一种用户属性信息在一种药物作用下对应一个用药指标。
It can be understood that assuming that each medication index prediction network is the above medication index prediction network 1 to medication index prediction network n, the computer equipment can determine the target user attribute information X 1 medication under the action of drug a through the above medication index prediction network 1 indicator f 1a (X 1) and medication index f 1b (X 1) under b action of drugs, by the above treatment predictor network 2 to determine the target user target user attribute information X 2 medication index at the drug a function f 2a ( X 2 ) and the medication index f 2b (X 2 ) under the action of the drug b. So, the computer device determines the target user via the network administration predictor n X n target user attribute information in the drug action of a drug f index Na (X n) and f indicators medication effects of drugs under b Nb (X n ), where a kind of user attribute information corresponds to a medication index under the action of a kind of medicine.
步骤S103,基于用药指标预测模型确定出任一药物作用下至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标。Step S103: Determine the target drug use indicators corresponding to at least two target user attribute information under the action of any drug based on the drug use index prediction model to obtain multiple target drug use indicators corresponding to multiple drugs.
在一些可行的实施方式中,用药指标预测模型对应的模型参数中可以包含各药物(如 上述药物a和药物b)对应的偏置参数(如上述偏置参数β
a和偏置参数β
b),一种药物对应一个偏置参数(如上述药物a对应偏置参数β
a和药物b对应偏置参数β
b)。计算机设备可以基于用药指标预测模型对各目标用户属性信息在任一药物作用下的各用药指标和任一药物对应的偏置参数进行累加,得到任一药物作用下至少两种目标用户属性信息对应的目标用药指标。假设目标用药指标为目标用户在上述药物a作用下的目标用药指标Y
a,可以用下述公式(1)确定Y
a:
In some feasible embodiments, the model parameters corresponding to the medication index prediction model may include the bias parameters corresponding to each drug (such as the above-mentioned drug a and the above-mentioned drug b) (such as the above-mentioned bias parameter β a and the bias parameter β b ) , A drug corresponds to a bias parameter (for example, the above-mentioned drug a corresponds to the bias parameter β a and the drug b corresponds to the bias parameter β b ). The computer equipment can accumulate the medication index of each target user attribute information under the action of any drug and the bias parameter corresponding to any drug based on the drug index prediction model to obtain at least two target user attribute information corresponding to any drug action Target medication index. Assuming that the target medication index is the target medication index Y a of the target user under the action of the above-mentioned drug a, the following formula (1) can be used to determine Y a :
Y
a=f
1a(X
1)+f
2a(X
2)+…+f
na(X
n)+β
a,(1)
Y a =f 1a (X 1 )+f 2a (X 2 )+…+f na (X n )+β a , (1)
其中,f
1a(X
1)可以表示目标用户的目标用户属性信息X
1在药物a作用下的用药指标,f
2a(X
2)可以表示目标用户的目标用户属性信息X
2在药物a作用下的用药指标,…,f
na(X
n)可以表示目标用户的目标用户属性信息X
n在药物a作用下的用药指标,β
a可以表示药物a对应的偏置参数。
Among them, f 1a (X 1 ) can represent the target user's target user attribute information X 1 medication index under the action of drug a, f 2a (X 2 ) can represent the target user's target user attribute information X 2 under the action of drug a The medication index of ,..., f na (X n ) can represent the medication index of the target user attribute information X n of the target user under the action of the drug a, and β a can represent the bias parameter corresponding to the drug a.
假设目标用药指标为目标用户在上述药物b作用下的目标用药指标Y
b,可以用下述公式(2)确定Y
b:
Assuming that the target medication index is the target medication index Y b of the target user under the action of the above-mentioned medication b, the following formula (2) can be used to determine Y b :
Y
b=f
1b(X
1)+f
2b(X
2)+…+f
nb(X
n)+β
b,(2)
Y b =f 1b (X 1 )+f 2b (X 2 )+…+f nb (X n )+β b , (2)
其中,f
1b(X
1)可以表示目标用户的目标用户属性信息X
1在药物b作用下的用药指标,f
2b(X
2)可以表示目标用户的目标用户属性信息X
2在药物b作用下的用药指标,…,f
nb(X
n)可以表示目标用户的目标用户属性信息X
n在药物b作用下的用药指标,β
b可以表示药物b对应的偏置参数。
Among them, f 1b (X 1 ) can represent the target user's target user attribute information X 1 medication index under the action of drug b, f 2b (X 2 ) can represent the target user's target user attribute information X 2 under the action of drug b The medication index of,..., f nb (X n ) can represent the medication index of the target user attribute information X n of the target user under the action of the drug b , and β b can represent the bias parameter corresponding to the drug b.
可以理解,计算机设备可以通过用药指标预测模型输出目标用户在多种药物作用下对应的多个目标用药指标(如上述药物a作用下的目标用药指标Y
a或者药物b作用下的目标用药指标Y
b),其中,目标用户在一种药物作用下对应一个目标用药指标。
It can be understood that the computer device can output multiple target medication indicators corresponding to the target user under the action of multiple drugs through the medication indicator prediction model (for example, the target medication index Y a under the action of drug a or the target medication indicator Y under the action of drug b. b ), where the target user corresponds to a target medication index under the action of a drug.
步骤S104,从多个目标用药指标中确定出最大目标用药指标,并将具有最大目标用药指标的目标药物推送给目标用户。Step S104: Determine the maximum target medication index from the multiple target medication indicators, and push the target drug with the largest target medication index to the target user.
在一些可行的实施方式中,计算机设备可以对用药指标预测模型输出的多个目标用药指标进行排序(比如从大到小排序或者从小到大排序),得到目标用药指标序列,并将目标用药指标序列中的第一个或者最后一个目标用药指标作为最大目标用药指标。此时,计算机设备可以将具有最大目标用药指标的目标药物推送给目标用户终端的用户界面(如上述CDSS的用户界面或者自助机器的用户界面),这时目标用户可以该用户界面上查看该目标 药物。In some feasible implementations, the computer device can sort multiple target medication indicators output by the medication indicator prediction model (for example, sort from large to small or from small to large) to obtain a sequence of target medication indicators, and compare the target medication indicators The first or last target medication index in the sequence is used as the maximum target medication index. At this time, the computer device can push the target drug with the largest target medication index to the user interface of the target user terminal (such as the user interface of the above-mentioned CDSS or the user interface of the self-service machine), and the target user can view the target on the user interface. drug.
在一些可行的实施方式中,多种药物包括目标药物以及目标药物之外的至少一种其它药物,目标用户属性信息在目标药物作用下的用药指标为第一用药指标,目标用户属性信息在其它药物作用下的用药指标为第二用药指标。计算机设备可以基于第一用药指标和第二用药指标确定优化用药指标,并将优化用药指标以可视化的形式(比如,图和/或表)展示给目标用户的用户界面,便于目标用户在该用户界面上查看优化用药指标。其中,优化用药指标可以为第一用药指标和第二用药指标之间的用药指标差值,该优化用药指标用于指示目标用户在一种目标用户属性信息下使用目标药物相对于使用第二药物的优势。由此可见,计算机设备可以将优化用药指标以可视化的形式展示给目标用户,目标用户可以直观的看到推荐目标药物的理由,从而提高了模型的可解释性,进而提高了用药推荐的安全性和用户粘性。In some feasible implementations, the multiple drugs include the target drug and at least one other drug other than the target drug, the medication index of the target user attribute information under the action of the target drug is the first medication index, and the target user attribute information is in other drugs. The medication index under the action of the drug is the second medication index. The computer device can determine the optimized medication index based on the first medication index and the second medication index, and display the optimized medication index to the user interface of the target user in a visual form (for example, a graph and/or table), so that the target user can easily access the user interface. View optimized medication indicators on the interface. Wherein, the optimized medication index may be the medication index difference between the first medication index and the second medication index. The optimized medication index is used to instruct the target user to use the target drug with respect to the use of the second drug under a kind of target user attribute information. The advantages. It can be seen that the computer equipment can display the optimized medication index to the target user in a visual form, and the target user can intuitively see the reason for recommending the target drug, thereby improving the interpretability of the model and improving the safety of medication recommendation Be sticky with users.
在本申请中,计算机设备可以通过用药指标预测模型中的各用药指标预测网络确定目标用户的各目标用户属性信息在各药物作用下的各用药指标,进而基于用药指标预测模型确定出任一药物作用下至少两种目标用户属性信息对应的目标用药指标,这里通过各用药指标预测网络确定出各用药指标,进一步根据各用药指标确定目标用药指标,从而提高了模型预测目标用药指标的精确度。进一步地,计算机设备可以基于用药指标预测模型确定出多种药物作用下对应的多个目标用药指标,并将具有最大目标用药指标的目标药物推送给目标用户,从而可以向用户精准推荐用药,提高了模型药物推荐的精确度,同时提高了药物推荐的用户粘性,适用性强。In this application, the computer device can determine the medication index of each target user attribute information of each target user under the action of each drug through the medication index prediction network in the medication index prediction model, and then determine the effect of any drug based on the medication index prediction model Below are at least two target medication indicators corresponding to the target user attribute information. Here, each medication indicator is determined through the medication indicator prediction network, and the target medication indicator is further determined according to each medication indicator, thereby improving the accuracy of the model predicting the target medication indicator. Further, the computer device can determine multiple target medication indicators corresponding to multiple drugs based on the medication indicator prediction model, and push the target drug with the largest target medication indicator to the target user, thereby accurately recommending medication to the user and improving The accuracy of model drug recommendation is improved, while the user stickiness of drug recommendation is improved, and the applicability is strong.
进一步地,请参见图4,图4是本申请提供的药物推荐装置的结构示意图。该药物推荐装置可以是运行于计算机设备中的一个计算机程序(包括程序代码),例如,该药物推荐装置为一个应用软件;该药物推荐装置可以用于执行本申请提供的方法中的相应步骤。如图4所示,该药物推荐装置1可以包括:第二获取模块10、第三获取模块20、第一获取模块30、第一确定模块40、第二确定模块50、推送模块60以及展示模块70。Further, please refer to FIG. 4, which is a schematic structural diagram of the drug recommendation device provided by the present application. The medication recommendation device may be a computer program (including program code) running in a computer device. For example, the medication recommendation device is an application software; the medication recommendation device may be used to perform corresponding steps in the method provided in this application. As shown in FIG. 4, the medication recommendation device 1 may include: a second acquisition module 10, a third acquisition module 20, a first acquisition module 30, a first determination module 40, a second determination module 50, a push module 60, and a display module 70.
第一获取模块30,用于获取目标用户的至少两种目标用户属性信息,将至少两种目标用户属性信息输入用药指标预测模型,用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;The first obtaining module 30 is configured to obtain at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into the medication index prediction model. The medication index prediction model includes at least two medication index prediction networks, one medication The index prediction network is used to output multiple medication indicators of a user attribute information of the user under the action of multiple drugs;
第一确定模块40,用于基于用药指标预测模型中的各用药指标预测网络确定目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;The first determining module 40 is used to determine the medication index of each target user attribute information of the target user under the action of each drug based on the drug usage index prediction network in the medication index prediction model, and one type of target user attribute information is under the action of a drug Corresponding to a medication index;
第二确定模块50,用于基于用药指标预测模型确定出任一药物作用下至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;The second determining module 50 is configured to determine the target drug use indicators corresponding to at least two target user attribute information under the action of any drug based on the drug use indicator prediction model to obtain multiple target drug indicators corresponding to multiple drugs;
推送模块60,用于从多个目标用药指标中确定出最大目标用药指标,并将具有最大目标用药指标的目标药物推送给目标用户。The pushing module 60 is used to determine the maximum target medication index from a plurality of target medication indicators, and push the target drug with the largest target medication index to the target user.
在一些可行的实施方式中,至少两种目标用户属性信息包括目标用户的年龄、性别以及针对目标疾病的健康指标中的至少一种。In some feasible implementation manners, the at least two types of target user attribute information include at least one of the target user's age, gender, and health indicators for the target disease.
在一些可行的实施方式中,多种药物包括目标药物以及目标药物之外的至少一种其它药物,目标用户属性信息在目标药物作用下的用药指标为第一用药指标,目标用户属性信息在其它药物作用下的用药指标为第二用药指标;In some feasible implementations, the multiple drugs include the target drug and at least one other drug other than the target drug, the medication index of the target user attribute information under the action of the target drug is the first medication index, and the target user attribute information is in other drugs. The medication index under the action of the drug is the second medication index;
上述药物推荐装置1还包括:The above-mentioned drug recommendation device 1 further includes:
展示模块70,用于基于第一用药指标和第二用药指标确定优化用药指标,并将优化用药指标展示给目标用户。The display module 70 is configured to determine an optimized medication index based on the first medication index and the second medication index, and display the optimized medication index to the target user.
在一些可行的实施方式中,上述药物推荐装置1还包括:In some feasible implementation manners, the above-mentioned drug recommendation device 1 further includes:
第二获取模块10,用于获取至少两个用户的样本数据,一个用户的样本数据包括用户的至少两种用户属性信息和用户在样本药物作用下的目标样本用药指标;The second acquisition module 10 is configured to acquire sample data of at least two users, and the sample data of one user includes at least two user attribute information of the user and the user's target sample medication index under the action of the sample drug;
第三获取模块20,用于将至少两个用户的样本数据输入用药指标预测模型,通过用药指标预测模型中的各用药指标预测网络对各用药指标预测网络对应的用户属性信息进行联合学习以获取预测任一用户的各用户属性信息在各药物作用下的用药指标的能力。The third acquisition module 20 is used to input the sample data of at least two users into the medication index prediction model, and perform joint learning on the user attribute information corresponding to each medication index prediction network through each medication index prediction network in the medication index prediction model to obtain The ability to predict the medication index of each user attribute information of any user under the action of each drug.
在一些可行的实施方式中,用药指标预测模型包含各药物对应的偏置参数,一种药物对应一个偏置参数;In some feasible embodiments, the medication index prediction model includes bias parameters corresponding to each drug, and one drug corresponds to one bias parameter;
第二确定模块50包括:累加单元501。The second determining module 50 includes: an accumulating unit 501.
累加单元501,用于基于用药指标预测模型对各目标用户属性信息在任一药物作用下的各用药指标和任一药物对应的偏置参数进行累加,得到任一药物作用下至少两种目标用户属性信息对应的目标用药指标。The accumulation unit 501 is used to accumulate the medication index of each target user attribute information under the action of any drug and the bias parameter corresponding to any drug based on the medication index prediction model to obtain at least two target user attributes under the action of any drug The target medication index corresponding to the information.
其中,该累加单元501的具体实现方式可以参见上述图2所对应实施例中对步骤S103的描述,这里将不再继续进行赘述。For the specific implementation of the accumulating unit 501, reference may be made to the description of step S103 in the embodiment corresponding to FIG. 2, which will not be repeated here.
其中,该第二获取模块10、第三获取模块20、第一获取模块30、第一确定模块40、第二确定模块50、推送模块60以及展示模块70的具体实现方式可以参见上述图2所对应实施例中对步骤S101-步骤S104的描述,这里将不再继续进行赘述。另外,对采用相同方法的有益效果描述,也不再进行赘述。Among them, the specific implementation of the second acquisition module 10, the third acquisition module 20, the first acquisition module 30, the first determination module 40, the second determination module 50, the push module 60, and the display module 70 can be found in the above-mentioned FIG. 2 Corresponding to the description of step S101 to step S104 in the embodiment, the description will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated.
进一步地,请参见图5,图5是本申请提供的计算机设备的结构示意图。如图5所示,该计算机设备1000可以为上述图1对应实施例中的服务器10,该计算机设备1000可以包括:至少一个处理器1001,例如CPU,至少一个网络接口1004,用户接口1003,存储器1005,至少一个通信总线1002。其中,通信总线1002用于实现这些组件之间的连接通信。其中,用户接口1003可以包括显示屏(display)、键盘(keyboard),网络接口1004可选地可以包括标准的有线接口、无线接口(如WI-FI接口)。存储器1005可以是高速随机存储记忆体(random access memory,RAM)存储器,也可以是非不稳定的存储器(non-volatile memory),例如至少一个磁盘存储器。存储器1005可选地还可以是至少一个位于远离前述处理器1001的存储装置。如图5所示,作为一种计算机存储介质的存储器1005中可以包括操作***、网络通信模块、用户接口模块以及设备控制应用程序。Further, please refer to FIG. 5, which is a schematic diagram of the structure of the computer device provided by the present application. As shown in FIG. 5, the computer device 1000 may be the server 10 in the embodiment corresponding to FIG. 1. The computer device 1000 may include: at least one processor 1001, such as a CPU, at least one network interface 1004, a user interface 1003, and a memory 1005, at least one communication bus 1002. Among them, the communication bus 1002 is used to implement connection and communication between these components. The user interface 1003 may include a display (display) and a keyboard (keyboard), and the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed random access memory (RAM) memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory 1005 may also be at least one storage device located far away from the aforementioned processor 1001. As shown in FIG. 5, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a device control application program.
在图5所示的计算机设备1000中,网络接口1004主要用于与用户终端进行网络通信;而用户接口1003主要用于为用户提供输入的接口;而处理器1001可以用于调用存储器1005中存储的设备控制应用程序,以实现:In the computer device 1000 shown in FIG. 5, the network interface 1004 is mainly used for network communication with a user terminal; and the user interface 1003 is mainly used to provide an input interface for the user; and the processor 1001 can be used to call the storage in the memory 1005 The device control application to achieve:
获取目标用户的至少两种目标用户属性信息,将至少两种目标用户属性信息输入用药指标预测模型,用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;Obtain at least two target user attribute information of the target user, and input the at least two target user attribute information into the medication index prediction model. The medication index prediction model includes at least two medication index prediction networks, and one medication index prediction network is used to output the user’s information Multiple medication indicators of a user attribute information under the action of multiple drugs;
基于用药指标预测模型中的各用药指标预测网络确定目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;Based on the medication index prediction network in the medication index prediction model, determine the medication index of each target user attribute information of the target user under the action of each drug, and one type of target user attribute information corresponds to a medication index under the action of a drug;
基于用药指标预测模型确定出任一药物作用下至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;Determine the target drug use indicators corresponding to at least two target user attribute information under the action of any drug based on the drug use index prediction model to obtain multiple target drug use indicators corresponding to multiple drugs;
从多个目标用药指标中确定出最大目标用药指标,并将具有最大目标用药指标的目标药物推送给目标用户。Determine the maximum target medication index from multiple target medication indicators, and push the target drug with the largest target medication index to the target user.
应当理解,本申请中所描述的计算机设备1000可执行前文图2所对应实施例中对该药物推荐方法的描述,也可执行前文图4所对应实施例中对该药物推荐装置1的描述,在此不再赘述。另外,对采用相同方法的有益效果描述,也不再进行赘述。It should be understood that the computer device 1000 described in this application can execute the description of the drug recommendation method in the foregoing embodiment corresponding to FIG. 2 and the description of the drug recommendation device 1 in the foregoing embodiment corresponding to FIG. 4, I won't repeat them here. In addition, the description of the beneficial effects of using the same method will not be repeated.
此外,这里需要指出的是:本申请还提供了一种计算机可读存储介质,且该计算机可读存储介质中存储有前文提及的药物推荐装置1所执行的计算机程序,且该计算机程序包 括程序指令,当该处理器执行该程序指令时,能够执行前文图2所对应实施例中对该药物推荐方法的描述,因此,这里将不再进行赘述。另外,对采用相同方法的有益效果描述,也不再进行赘述。对于本申请所涉及的计算机可读存储介质实施例中未披露的技术细节,请参照本申请方法实施例的描述。作为示例,程序指令可被部署为在一个计算设备上执行,或者在位于一个地点的多个计算设备上执行,又或者,在分布在多个地点且通过通信网络互连的多个计算设备上执行,分布在多个地点且通过通信网络互连的多个计算设备可以组成区块链***。In addition, it should be pointed out here that this application also provides a computer-readable storage medium, and the computer-readable storage medium stores the aforementioned computer program executed by the drug recommendation device 1, and the computer program includes Program instructions. When the processor executes the program instructions, it can execute the description of the drug recommendation method in the embodiment corresponding to FIG. 2 above, and therefore, it will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated. For technical details that are not disclosed in the embodiment of the computer-readable storage medium involved in this application, please refer to the description of the method embodiment of this application. As an example, the program instructions may be deployed to be executed on one computing device, or on multiple computing devices located in one location, or on multiple computing devices that are distributed in multiple locations and interconnected by a communication network Implementation, multiple computing devices distributed in multiple locations and interconnected through a communication network can form a blockchain system.
可选的,本申请涉及的存储介质如计算机可读存储介质可以是非易失性的,也可以是易失性的。Optionally, the storage medium involved in this application, such as a computer-readable storage medium, may be non-volatile or volatile.
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程,是可以通过计算机程序来指令相关的硬件来完成,上述的程序可存储于一计算机可读取存储介质中,该程序在执行时,可包括如上述各方法的实施例的流程。其中,上述计算机可读存储介质可以是前述任一实施例提供的药物推荐装置或者上述设备的内部存储单元,例如电子设备的硬盘或内存。该计算机可读存储介质也可以是该电子设备的外部存储设备,例如该电子设备上配备的插接式硬盘,智能存储卡(smart media card,SMC),安全数字(secure digital,SD)卡,闪存卡(flash card)等。上述计算机可读存储介质还可以包括磁碟、光盘、只读存储记忆体(read-only memory,ROM)或RAM等。进一步地,该计算机可读存储介质还可以既包括该电子设备的内部存储单元也包括外部存储设备。该计算机可读存储介质用于存储该计算机程序以及该电子设备所需的其它程序和数据。该计算机可读存储介质还可以用于暂时地存储已经输出或者将要输出的数据。A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing relevant hardware through a computer program. The above-mentioned program can be stored in a computer readable storage medium. When executed, it may include the procedures of the above-mentioned method embodiments. The foregoing computer-readable storage medium may be the medication recommendation device provided in any of the foregoing embodiments or the internal storage unit of the foregoing device, such as a hard disk or memory of an electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), or a secure digital (SD) card equipped on the electronic device. Flash card, etc. The above-mentioned computer-readable storage medium may also include a magnetic disk, an optical disk, a read-only memory (read-only memory, ROM), or RAM. Further, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.
本申请的权利要求书和说明书及附图中的术语“第一”、“第二”等是用于区别不同对象,而不是用于描述特定顺序。此外,术语“包括”和“具有”以及它们任何变形,意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、***、产品或设备没有限定于已列出的步骤或单元,而是可选地还包括没有列出的步骤或单元,或可选地还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。在本文中提及“实施例”意味着,结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置展示该短语并不一定均是指相同的实施例,也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是,本文所描述的实施例可以与其它实施例相结合。在本申请说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合,并且包括这些组合。The terms "first" and "second" in the claims, specification and drawings of the present application are used to distinguish different objects, rather than to describe a specific sequence. In addition, the terms "including" and "having" and any variations of them are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes unlisted steps or units, or optionally also includes Other steps or units inherent to these processes, methods, products or equipment. The reference to "embodiments" herein means that a specific feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The display of the phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art clearly and implicitly understand that the embodiments described herein can be combined with other embodiments. The term "and/or" used in the specification of this application and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.
本领域普通技术人员可以意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,能够以电子硬件、计算机软件或者二者的结合来实现,为了清楚地说明硬件和软件的可互换性,在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本申请的范围。A person of ordinary skill in the art may be aware that the units and algorithm steps of the examples described in the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both, in order to clearly illustrate the hardware and software Interchangeability, in the above description, the composition and steps of each example have been generally described in accordance with the function. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered beyond the scope of this application.
以上所揭露的仅为本申请较佳实施例而已,当然不能以此来限定本申请之权利范围,因此依本申请权利要求所作的等同变化,仍属本申请所涵盖的范围。The above-disclosed are only preferred embodiments of this application, and of course the scope of rights of this application cannot be limited by this. Therefore, equivalent changes made in accordance with the claims of this application still fall within the scope of this application.
Claims (20)
- 一种药物推荐方法,包括:A method of drug recommendation, including:获取目标用户的至少两种目标用户属性信息,将所述至少两种目标用户属性信息输入用药指标预测模型,所述用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;Obtain at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into the medication index prediction model. The medication index prediction model includes at least two medication index prediction networks, one medication index prediction network uses To output multiple medication indicators of a user attribute information of the user under the action of multiple drugs;基于所述用药指标预测模型中的各用药指标预测网络确定所述目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;Based on the medication index prediction network in the medication index prediction model, determine the medication index of each target user attribute information of the target user under the action of each drug, and one type of target user attribute information corresponds to a medication index under the action of a drug ;基于所述用药指标预测模型确定出任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;Determine the target drug use indicators corresponding to the at least two target user attribute information under the action of any drug based on the drug use index prediction model to obtain multiple target drug use indicators corresponding to multiple drugs;从所述多个目标用药指标中确定出最大目标用药指标,并将具有所述最大目标用药指标的目标药物推送给所述目标用户。The maximum target drug use index is determined from the multiple target drug use indicators, and the target drug having the maximum target drug use index is pushed to the target user.
- 根据权利要求1所述的方法,其中,所述多种药物包括所述目标药物以及所述目标药物之外的至少一种其它药物,所述目标用户属性信息在所述目标药物作用下的用药指标为第一用药指标,所述目标用户属性信息在所述其它药物作用下的用药指标为第二用药指标;The method according to claim 1, wherein the multiple drugs include the target drug and at least one other drug other than the target drug, and the target user attribute information is used under the action of the target drug The index is the first medication index, and the medication index of the target user attribute information under the action of the other drugs is the second medication index;所述方法还包括:The method also includes:基于所述第一用药指标和所述第二用药指标确定优化用药指标,并将所述优化用药指标展示给所述目标用户。An optimized medication index is determined based on the first medication index and the second medication index, and the optimized medication index is displayed to the target user.
- 根据权利要求1或2所述的方法,其中,所述方法还包括:The method according to claim 1 or 2, wherein the method further comprises:获取至少两个用户的样本数据,一个用户的样本数据包括所述用户的至少两种用户属性信息和所述用户在样本药物作用下的目标样本用药指标;Acquiring sample data of at least two users, where the sample data of one user includes at least two user attribute information of the user and the target sample medication index of the user under the action of the sample drug;将所述至少两个用户的样本数据输入所述用药指标预测模型,通过所述用药指标预测模型中的各用药指标预测网络对所述各用药指标预测网络对应的用户属性信息进行联合学习以获取预测任一用户的各用户属性信息在各药物作用下的用药指标的能力。The sample data of the at least two users are input into the medication index prediction model, and the user attribute information corresponding to each medication index prediction network is jointly learned through each medication index prediction network in the medication index prediction model to obtain The ability to predict the medication index of each user attribute information of any user under the action of each drug.
- 根据权利要求3所述的方法,其中,所述用药指标预测模型包含所述各药物对应的偏置参数,一种药物对应一个偏置参数;The method according to claim 3, wherein the medication index prediction model comprises a bias parameter corresponding to each of the medications, and one medication corresponds to a bias parameter;所述基于所述用药指标预测模型确定出任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标,包括:The determining the target drug use index corresponding to the at least two target user attribute information under the action of any drug based on the drug use index prediction model includes:基于所述用药指标预测模型对各目标用户属性信息在任一药物作用下的各用药指标和所述任一药物对应的偏置参数进行累加,得到所述任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标。Based on the medication index prediction model, the medication index of each target user attribute information under the action of any drug and the bias parameter corresponding to the any drug are accumulated to obtain the at least two targets under the action of any drug Target medication index corresponding to user attribute information.
- 根据权利要求1所述的方法,其中,所述至少两种目标用户属性信息包括所述目标用户的年龄、性别以及针对目标疾病的健康指标中的至少一种。The method according to claim 1, wherein the at least two types of target user attribute information include at least one of the target user's age, gender, and health indicators for target diseases.
- 一种药物推荐装置,包括:A drug recommendation device, including:第一获取模块,用于获取目标用户的至少两种目标用户属性信息,将所述至少两种目标用户属性信息输入用药指标预测模型,所述用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;The first acquisition module is configured to acquire at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into a medication index prediction model, and the medication index prediction model includes at least two medication index prediction networks , A medication index prediction network is used to output multiple medication indicators of a user attribute information of the user under the action of multiple drugs;第一确定模块,用于基于所述用药指标预测模型中的各用药指标预测网络确定所述目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;The first determining module is used to determine the medication index of each target user attribute information of the target user under the action of each drug based on the drug use index prediction network in the drug use index prediction model, one type of target user attribute information is in one type Corresponding to a medication index under the action of the drug;第二确定模块,用于基于所述用药指标预测模型确定出任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;The second determining module is configured to determine the target drug use indicators corresponding to the at least two target user attribute information under the action of any drug based on the drug use indicator prediction model to obtain multiple target drug use indicators corresponding to multiple drugs;推送模块,用于从所述多个目标用药指标中确定出最大目标用药指标,并将具有所述最大目标用药指标的目标药物推送给所述目标用户。The push module is used to determine the maximum target drug use index from the multiple target drug use indicators, and push the target drug with the maximum target drug use index to the target user.
- 根据权利要求6所述的装置,其中,所述多种药物包括所述目标药物以及所述目标药物之外的至少一种其它药物,所述目标用户属性信息在所述目标药物作用下的用药指标为第一用药指标,所述目标用户属性信息在所述其它药物作用下的用药指标为第二用药指标;7. The device according to claim 6, wherein the multiple drugs include the target drug and at least one other drug other than the target drug, and the target user attribute information is used under the action of the target drug The index is the first medication index, and the medication index of the target user attribute information under the action of the other drugs is the second medication index;所述装置还包括:The device also includes:展示模块,用于基于所述第一用药指标和所述第二用药指标确定优化用药指标,并将所述优化用药指标展示给所述目标用户。The display module is configured to determine an optimized medication index based on the first medication index and the second medication index, and display the optimized medication index to the target user.
- 根据权利要求6或7所述的装置,其中,所述装置还包括:The device according to claim 6 or 7, wherein the device further comprises:第二获取模块,用于获取至少两个用户的样本数据,一个用户的样本数据包括所述用户的至少两种用户属性信息和所述用户在样本药物作用下的目标样本用药指标;The second acquisition module is configured to acquire sample data of at least two users, and the sample data of one user includes at least two user attribute information of the user and the target sample medication index of the user under the action of the sample drug;第三获取模块,用于将所述至少两个用户的样本数据输入所述用药指标预测模型,通过所述用药指标预测模型中的各用药指标预测网络对所述各用药指标预测网络对应的用户属性信息进行联合学习以获取预测任一用户的各用户属性信息在各药物作用下的用药指标的能力。The third acquisition module is configured to input the sample data of the at least two users into the medication index prediction model, and use the medication index prediction network in the medication index prediction model to predict the user corresponding to each medication index network The attribute information is jointly learned to obtain the ability to predict the medication index of each user's attribute information of any user under the action of each drug.
- 根据权利要求8所述的装置,其中,所述用药指标预测模型包含所述各药物对应的偏置参数,一种药物对应一个偏置参数;8. The device of claim 8, wherein the medication index prediction model comprises a bias parameter corresponding to each of the medications, and one type of medication corresponds to a bias parameter;所述第二确定模块具体用于:The second determining module is specifically configured to:基于所述用药指标预测模型对各目标用户属性信息在任一药物作用下的各用药指标和所述任一药物对应的偏置参数进行累加,得到所述任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标。Based on the medication index prediction model, the medication index of each target user attribute information under the action of any drug and the bias parameter corresponding to the any drug are accumulated to obtain the at least two targets under the action of any drug Target medication index corresponding to user attribute information.
- 根据权利要求6所述的装置,其中,所述至少两种目标用户属性信息包括所述目标用户的年龄、性别以及针对目标疾病的健康指标中的至少一种。7. The device according to claim 6, wherein the at least two types of target user attribute information include at least one of the target user's age, gender, and health indicators for target diseases.
- 一种计算机设备,包括:处理器、存储器以及网络接口;A computer device, including: a processor, a memory, and a network interface;所述处理器与存储器、网络接口相连,其中,网络接口用于提供数据通信功能,所述存储器用于存储程序代码,所述处理器用于调用所述程序代码,执行以下方法:The processor is connected to a memory and a network interface, where the network interface is used to provide data communication functions, the memory is used to store program codes, and the processor is used to call the program codes to execute the following methods:获取目标用户的至少两种目标用户属性信息,将所述至少两种目标用户属性信息输入用药指标预测模型,所述用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;Obtain at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into the medication index prediction model. The medication index prediction model includes at least two medication index prediction networks, one medication index prediction network uses To output multiple medication indicators of a user attribute information of the user under the action of multiple drugs;基于所述用药指标预测模型中的各用药指标预测网络确定所述目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;Based on the medication index prediction network in the medication index prediction model, determine the medication index of each target user attribute information of the target user under the action of each drug, and one type of target user attribute information corresponds to a medication index under the action of a drug ;基于所述用药指标预测模型确定出任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;Determine the target drug use indicators corresponding to the at least two target user attribute information under the action of any drug based on the drug use index prediction model to obtain multiple target drug use indicators corresponding to multiple drugs;从所述多个目标用药指标中确定出最大目标用药指标,并将具有所述最大目标用药指标的目标药物推送给所述目标用户。The maximum target drug use index is determined from the multiple target drug use indicators, and the target drug having the maximum target drug use index is pushed to the target user.
- 根据权利要求11所述的计算机设备,其中,所述多种药物包括所述目标药物以及所述目标药物之外的至少一种其它药物,所述目标用户属性信息在所述目标药物作用下的用药指标为第一用药指标,所述目标用户属性信息在所述其它药物作用下的用药指标为第二用药指标;The computer device according to claim 11, wherein the multiple drugs include the target drug and at least one other drug other than the target drug, and the target user attribute information is affected by the target drug The medication index is the first medication index, and the medication index of the target user attribute information under the action of the other medication is the second medication index;所述处理器还用于执行:The processor is also used to execute:基于所述第一用药指标和所述第二用药指标确定优化用药指标,并将所述优化用药指标展示给所述目标用户。An optimized medication index is determined based on the first medication index and the second medication index, and the optimized medication index is displayed to the target user.
- 根据权利要求11或12所述的计算机设备,其中,所述处理器还用于执行:The computer device according to claim 11 or 12, wherein the processor is further configured to execute:获取至少两个用户的样本数据,一个用户的样本数据包括所述用户的至少两种用户属性信息和所述用户在样本药物作用下的目标样本用药指标;Acquiring sample data of at least two users, where the sample data of one user includes at least two user attribute information of the user and the target sample medication index of the user under the action of the sample drug;将所述至少两个用户的样本数据输入所述用药指标预测模型,通过所述用药指标预测模型中的各用药指标预测网络对所述各用药指标预测网络对应的用户属性信息进行联合学习以获取预测任一用户的各用户属性信息在各药物作用下的用药指标的能力。The sample data of the at least two users are input into the medication index prediction model, and the user attribute information corresponding to each medication index prediction network is jointly learned through each medication index prediction network in the medication index prediction model to obtain The ability to predict the medication index of each user attribute information of any user under the action of each drug.
- 根据权利要求13所述的计算机设备,其中,所述用药指标预测模型包含所述各药物对应的偏置参数,一种药物对应一个偏置参数;11. The computer device according to claim 13, wherein the medication index prediction model comprises a bias parameter corresponding to each of the medications, and one type of medication corresponds to a bias parameter;所述基于所述用药指标预测模型确定出任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标时,具体执行:When the target medication index corresponding to the at least two types of target user attribute information under the action of any drug is determined based on the medication index prediction model, the following is specifically executed:基于所述用药指标预测模型对各目标用户属性信息在任一药物作用下的各用药指标和所述任一药物对应的偏置参数进行累加,得到所述任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标。Based on the medication index prediction model, the medication index of each target user attribute information under the action of any drug and the bias parameter corresponding to the any drug are accumulated to obtain the at least two targets under the action of any drug Target medication index corresponding to user attribute information.
- 根据权利要求11所述的计算机设备,其中,所述至少两种目标用户属性信息包括所述目标用户的年龄、性别以及针对目标疾病的健康指标中的至少一种。11. The computer device according to claim 11, wherein the at least two types of target user attribute information include at least one of the target user's age, gender, and health indicators for target diseases.
- 一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,所述计算机程序包括程序指令,所述程序指令被处理器执行时,执行以下方法:A computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the following method is executed:获取目标用户的至少两种目标用户属性信息,将所述至少两种目标用户属性信息输入用药指标预测模型,所述用药指标预测模型中包含至少两个用药指标预测网络,一个用药指标预测网络用于输出用户的一种用户属性信息在多种药物作用下的多个用药指标;Obtain at least two types of target user attribute information of the target user, and input the at least two types of target user attribute information into the medication index prediction model. The medication index prediction model includes at least two medication index prediction networks, one medication index prediction network uses To output multiple medication indicators of a user attribute information of the user under the action of multiple drugs;基于所述用药指标预测模型中的各用药指标预测网络确定所述目标用户的各目标用户属性信息在各药物作用下的用药指标,一种目标用户属性信息在一种药物作用下对应一个用药指标;Based on the medication index prediction network in the medication index prediction model, determine the medication index of each target user attribute information of the target user under the action of each drug, and one type of target user attribute information corresponds to a medication index under the action of a drug ;基于所述用药指标预测模型确定出任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标以得到多种药物对应的多个目标用药指标;Determine the target drug use indicators corresponding to the at least two target user attribute information under the action of any drug based on the drug use index prediction model to obtain multiple target drug use indicators corresponding to multiple drugs;从所述多个目标用药指标中确定出最大目标用药指标,并将具有所述最大目标用药指标的目标药物推送给所述目标用户。The maximum target drug use index is determined from the multiple target drug use indicators, and the target drug having the maximum target drug use index is pushed to the target user.
- 根据权利要求16所述的计算机可读存储介质,其中,所述多种药物包括所述目标药物以及所述目标药物之外的至少一种其它药物,所述目标用户属性信息在所述目标药物作用下的用药指标为第一用药指标,所述目标用户属性信息在所述其它药物作用下的用药指标为第二用药指标;The computer-readable storage medium according to claim 16, wherein the multiple drugs include the target drug and at least one other drug other than the target drug, and the target user attribute information is in the target drug The medication index under the action is the first medication index, and the medication index of the target user attribute information under the action of the other medication is the second medication index;所述程序指令被处理器执行时还用于执行:When the program instructions are executed by the processor, they are also used to execute:基于所述第一用药指标和所述第二用药指标确定优化用药指标,并将所述优化用药指标展示给所述目标用户。An optimized medication index is determined based on the first medication index and the second medication index, and the optimized medication index is displayed to the target user.
- 根据权利要求16或17所述的计算机可读存储介质,其中,所述程序指令被处理器执行时还用于执行:The computer-readable storage medium according to claim 16 or 17, wherein, when the program instructions are executed by the processor, they are also used to execute:获取至少两个用户的样本数据,一个用户的样本数据包括所述用户的至少两种用户属性信息和所述用户在样本药物作用下的目标样本用药指标;Acquiring sample data of at least two users, where the sample data of one user includes at least two user attribute information of the user and the target sample medication index of the user under the action of the sample drug;将所述至少两个用户的样本数据输入所述用药指标预测模型,通过所述用药指标预测模型中的各用药指标预测网络对所述各用药指标预测网络对应的用户属性信息进行联合学习以获取预测任一用户的各用户属性信息在各药物作用下的用药指标的能力。The sample data of the at least two users are input into the medication index prediction model, and the user attribute information corresponding to each medication index prediction network is jointly learned through each medication index prediction network in the medication index prediction model to obtain The ability to predict the medication index of each user attribute information of any user under the action of each drug.
- 根据权利要求18所述的计算机可读存储介质,其中,所述用药指标预测模型包含所述各药物对应的偏置参数,一种药物对应一个偏置参数;18. The computer-readable storage medium of claim 18, wherein the medication index prediction model comprises a bias parameter corresponding to each of the medications, and one type of medication corresponds to a bias parameter;所述基于所述用药指标预测模型确定出任一药物作用下所述至少两种目标用户属性信 息对应的目标用药指标时,具体执行:When the target drug use index corresponding to the at least two target user attribute information under the action of any drug is determined based on the drug use index prediction model, the following is specifically executed:基于所述用药指标预测模型对各目标用户属性信息在任一药物作用下的各用药指标和所述任一药物对应的偏置参数进行累加,得到所述任一药物作用下所述至少两种目标用户属性信息对应的目标用药指标。Based on the medication index prediction model, the medication index of each target user attribute information under the action of any drug and the bias parameter corresponding to the any drug are accumulated to obtain the at least two targets under the action of any drug Target medication index corresponding to user attribute information.
- 根据权利要求19所述的计算机可读存储介质,其中,所述至少两种目标用户属性信息包括所述目标用户的年龄、性别以及针对目标疾病的健康指标中的至少一种。The computer-readable storage medium according to claim 19, wherein the at least two types of target user attribute information include at least one of the target user's age, gender, and health indicators for target diseases.
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