Electronic medical record analysis method and system based on natural language processing

Abstract

The invention discloses an electronic medical record analysis method and system based on natural language processing, and the method comprises the steps: obtaining an electronic medical record of a user, dividing the electronic medical record into a plurality of information blocks, and enabling each information block to correspond to a certain element in the electronic medical record, extracting an embedded vector representation of each information block through a natural language model; setting a text mapping model of the electronic medical record, and obtaining global representation of the electronic medical record according to the embedded vector representation of the information block; setting a time sequence dynamic acquisition model of the electronic medical record, and acquiring the embedded vector representation of each time point of the information block according to the embedded vector representation of the information block; setting a disease and symptom prediction model, and predicting the disease type of the user according to the global representation of the electronic medical record and the embedded vector representation of each time point; and according to the prediction result, recommending a drug to the user, setting a curative effect evaluation model of the recommended drug, and evaluating the curative effect after the user takes the drug.

Description

Technical Field

[0001] The present invention belongs to the technical field of electronic medical record analysis, and more specifically, relates to an electronic medical record analysis method and system based on natural language processing. Background Art

[0002] Electronic Medical Record (EMR) refers to a system that records, stores and manages patient health information in digital form. Compared with traditional paper medical records, electronic medical records can more conveniently store, update, query and share patients' medical information, thereby improving the efficiency and quality of medical services.

[0003] Electronic medical records typically include the following: 1. Basic information: patient’s name, age, gender, contact information and other personal information.

[0004] 2. Medical history records: including the patient’s family history, personal medical history, allergy history, etc.

[0005] 3. Diagnostic information: the doctor’s records of the patient’s disease diagnosis, clinical symptoms, physical signs, etc.

[0006] 4. Treatment records: including medication records, surgical records, treatment plans, etc.

[0007] 5. Examination and test results: such as blood tests, imaging tests, pathological examinations, etc.

[0008] 6. Doctor’s orders: Doctor’s suggestions on the patient’s treatment plan, medication, nursing, rehabilitation, etc.

[0009] 7. Follow-up and re-examination records: follow-up tracking of the patient’s illness, recovery status, re-examination recommendations, etc.

[0010] 8. Electronic medical records can not only improve medical efficiency, but also realize information sharing and facilitate collaboration between different medical institutions. By integrating data from different departments (such as laboratories, imaging departments, etc.), electronic medical records provide doctors with comprehensive information support, which helps to improve the accuracy of diagnosis and treatment decisions.

[0011] However, the existing technologies are insufficient in their ability to analyze and process information in electronic medical records, and lack the ability to mine electronic medical record information. Summary of the invention

[0012] In order to solve the above technical problems, the present invention proposes an electronic medical record analysis method based on natural language processing, comprising: Obtaining the user's electronic medical record, dividing the electronic medical record into multiple information blocks, each of which corresponds to an element in the electronic medical record, and extracting an embedded vector representation of each of the information blocks through a natural language model, wherein the element is: user information, symptoms, medication name, and time taken for recovery; Setting a text mapping model for the electronic medical record, and obtaining a global representation of the electronic medical record according to the embedded vector representation of the information block; Setting a time series dynamic acquisition model for electronic medical records, and acquiring an embedded vector representation of each time point of the information block according to the embedded vector representation of the information block; Setting a disease and symptom prediction model, and predicting the disease type of the user based on the global representation of the electronic medical record and the embedded vector representation at each time point; Based on the prediction results, drugs are recommended to users, and an efficacy evaluation model for the recommended drugs is set up to evaluate the efficacy of the drugs after users take them.

[0013] Furthermore, the text mapping model of the electronic medical record includes: , in, Electronic Medical Records The global representation of is the number of information blocks, For the The adjustment factor for each block of information, is the activation function, is the weight matrix of the text mapping model, For the The embedding vector representation of each information block is: It is the bias term of the text mapping model.

[0014] Furthermore, the temporal dynamic acquisition model of electronic medical records includes: , in, For the The embedding vector representation of each time point is Dynamically obtain the model's weight matrix for time series, For the The embedding vector representation of each time point is Electronic Medical Records No. Time series data at a time point, Dynamically obtain the model's bias terms for time series.

[0015] Furthermore, the disease and symptom prediction model includes: , in, Electronic medical records Determine whether the user has a disease The probability of is the first weight matrix of the disease and symptom prediction model, is the second weight matrix of the disease and symptom prediction model, For the The embedding vector representation of each time point is is the bias term of the disease and symptom prediction model.

[0016] Furthermore, the efficacy evaluation model of the recommended drugs includes: , in, Electronic medical records The efficacy evaluation value of the recommended drug, For the types of drugs recommended, For the Adjustment factors for recommended drugs of this class, is the weight matrix of the efficacy evaluation model, For the The characteristics of the recommended drugs are shown in Figure 2. is the bias term of the efficacy evaluation model.

[0017] Furthermore, a drug efficacy evaluation model is set up to evaluate the efficacy of the drug after the user takes the drug, including: comparing the efficacy evaluation value with a preset evaluation threshold; when the preset evaluation threshold is exceeded, the recommended drug is effective; otherwise, the drug is recommended again.

[0018] The present invention also proposes an electronic medical record analysis system based on natural language processing, comprising: An information acquisition module is used to acquire the user's electronic medical record, divide the electronic medical record into multiple information blocks, each of which corresponds to an element in the electronic medical record, and extract an embedded vector representation of each information block through a natural language model, wherein the element is: user information, symptoms, medication name, and time taken for recovery; A text mapping module, used to set a text mapping model for the electronic medical record, and obtain a global representation of the electronic medical record according to the embedded vector representation of the information block; A time series dynamic acquisition module, used to set a time series dynamic acquisition model for electronic medical records, and acquire an embedded vector representation of each time point of the information block according to the embedded vector representation of the information block; A symptom prediction module, used to set a disease and symptom prediction model and predict the disease type of the user based on the global representation of the electronic medical record and the embedded vector representation of each time point; The efficacy evaluation module is used to recommend drugs to users based on the prediction results, set up an efficacy evaluation model for the recommended drugs, and evaluate the efficacy of the drugs after the users take them.

[0019] Furthermore, the text mapping model of the electronic medical record includes: , in, Electronic Medical Records The global representation of is the number of information blocks, For the The adjustment factor for each block of information, is the activation function, is the weight matrix of the text mapping model, For the The embedding vector representation of each information block is: It is the bias term of the text mapping model.

[0020] Furthermore, the temporal dynamic acquisition model of electronic medical records includes: , in, For the The embedding vector representation of each time point is Dynamically obtain the model's weight matrix for time series, For the The embedding vector representation of each time point is Electronic Medical Records No. Time series data at a time point, Dynamically obtain the model's bias terms for time series.

[0021] Furthermore, the disease and symptom prediction model includes: , in, Electronic medical records Determine whether the user has a disease The probability of is the first weight matrix of the disease and symptom prediction model, is the second weight matrix of the disease and symptom prediction model, For the The embedding vector representation of each time point is is the bias term of the disease and symptom prediction model.

[0022] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art: Through the above technical scheme, the present invention can extract key information from the user's electronic medical record, analyze the key information, thereby predicting the user's possible diseases, recommending drugs to the user, and judging the effects of the recommended drugs, thereby making adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of the method of embodiment 1 of the present invention; Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0024] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0026] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0027] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0028] The display screen is used to display the user interface of each application.

[0029] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0030] Example 1 like Figure 1 As shown, the embodiment of the present invention proposes an electronic medical record analysis method based on natural language processing, including: Step 101, obtain the user's electronic medical record, divide the electronic medical record into multiple information blocks, each of the information blocks corresponds to an element in the electronic medical record, and extract the embedding vector representation of each information block through a natural language model (such as BERT, ChatGPT, etc.), wherein the certain element is: user information, symptoms, medication name, and time taken for recovery; Step 102, setting a text mapping model for the electronic medical record, and obtaining a global representation of the electronic medical record according to the embedded vector representation of the information block; Specifically, the text mapping model of electronic medical records includes: , in, Electronic Medical Records The global representation of is the number of information blocks, For the The adjustment factor for each block of information, is the activation function (such as Sigmoid or tanh), is the weight matrix of the text mapping model, For the The embedding vector representation of each information block is: It is the bias term of the text mapping model.

[0031] Step 103, setting a temporal dynamic acquisition model for electronic medical records, and acquiring an embedded vector representation of each time point of the information block (such as the occurrence and change of symptoms, the use of drugs, etc.) according to the embedded vector representation of the information block; Specifically, the temporal dynamic acquisition model of electronic medical records includes: , in, For the The embedding vector representation of each time point is Dynamically obtain the model's weight matrix for time series, For the The embedding vector representation of each time point is Electronic Medical Records No. Time series data at a time point, Dynamically obtain the model's bias terms for time series.

[0032] Step 104, setting a disease and symptom prediction model, and predicting the user's disease type based on the global representation of the electronic medical record and the embedded vector representation of each time point; Specifically, the disease and symptom prediction models include: , in, Electronic medical records Determine whether the user has a disease The probability of is the first weight matrix of the disease and symptom prediction model, is the second weight matrix of the disease and symptom prediction model, For the The embedding vector representation of each time point is is the bias term of the disease and symptom prediction model.

[0033] Step 105, based on the prediction results, recommend drugs to the user, and set up an efficacy evaluation model for the recommended drugs to evaluate the efficacy of the drugs after the user takes them.

[0034] Specifically, the recommended drug efficacy evaluation model includes: , in, Electronic medical records The efficacy evaluation value of the recommended drug, For the types of drugs recommended, For the Adjustment factors for recommended drugs of this class, is the weight matrix of the efficacy evaluation model, For the Feature representation of recommended drugs (can be constructed based on the drug's chemical composition, dosage, etc.), is the bias term of the efficacy evaluation model.

[0035] Specifically, a drug efficacy evaluation model is set up to evaluate the efficacy of the drug after the user takes the drug, including: comparing the efficacy evaluation value with a preset evaluation threshold; when the preset evaluation threshold is exceeded, the recommended drug is effective; otherwise, the drug is recommended again.

[0036] Example 2 like Figure 2 As shown, an embodiment of the present invention further provides an electronic medical record analysis system based on natural language processing, comprising: An information acquisition module is used to acquire the user's electronic medical record, divide the electronic medical record into multiple information blocks, each of which corresponds to an element in the electronic medical record, and extract an embedded vector representation of each information block through a natural language model (such as BERT, ChatGPT, etc.), wherein the element is: user information, symptoms, medication name, and time taken for recovery; A text mapping module, used to set a text mapping model for the electronic medical record, and obtain a global representation of the electronic medical record according to the embedded vector representation of the information block; Specifically, the text mapping model of electronic medical records includes: , in, Electronic Medical Records The global representation of is the number of information blocks, For the The adjustment factor for each block of information, is the activation function, is the weight matrix of the text mapping model, For the The embedding vector representation of each information block is: It is the bias term of the text mapping model.

[0037] A time series dynamic acquisition module, used to set a time series dynamic acquisition model for electronic medical records, and acquire an embedded vector representation of each time point of the information block according to the embedded vector representation of the information block; Specifically, the temporal dynamic acquisition model of electronic medical records includes: , in, For the The embedding vector representation of each time point is Dynamically obtain the model's weight matrix for time series, For the The embedding vector representation of each time point is Electronic Medical Records No. Time series data at a time point, Dynamically obtain the model's bias terms for time series.

[0038] A symptom prediction module, used to set a disease and symptom prediction model and predict the disease type of the user based on the global representation of the electronic medical record and the embedded vector representation of each time point; Specifically, the disease and symptom prediction models include: , in, Electronic medical records Determine whether the user has a disease The probability of is the first weight matrix of the disease and symptom prediction model, is the second weight matrix of the disease and symptom prediction model, For the The embedding vector representation of each time point is is the bias term of the disease and symptom prediction model.

[0039] The efficacy evaluation module is used to recommend drugs to users based on the prediction results, set up an efficacy evaluation model for the recommended drugs, and evaluate the efficacy of the drugs after the users take them.

[0040] Specifically, the recommended drug efficacy evaluation model includes: , in, Electronic medical records The efficacy evaluation value of the recommended drug, For the types of drugs recommended, For the Adjustment factors for recommended drugs of this class, is the weight matrix of the efficacy evaluation model, For the The characteristics of the recommended drugs are shown in Figure 2. is the bias term of the efficacy evaluation model.

[0041] Specifically, a drug efficacy evaluation model is set up to evaluate the efficacy of the drug after the user takes the drug, including: comparing the efficacy evaluation value with a preset evaluation threshold; when the preset evaluation threshold is exceeded, the recommended drug is effective; otherwise, the drug is recommended again.

[0042] Example 3 An embodiment of the present invention further provides a storage medium storing a plurality of instructions, wherein the instructions are used to implement the electronic medical record analysis method based on natural language processing.

[0043] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0044] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining the electronic medical record of the user, dividing the electronic medical record into a plurality of information blocks, each of the information blocks corresponding to a certain element in the electronic medical record, and extracting an embedded vector representation of each of the information blocks through a natural language model (such as BERT, ChatGPT, etc.), wherein the certain element is: user information, symptoms, medication name, and time taken for recovery; Step 102, setting a text mapping model for the electronic medical record, and obtaining a global representation of the electronic medical record according to the embedded vector representation of the information block; Specifically, the text mapping model of electronic medical records includes: , in, Electronic Medical Records The global representation of is the number of information blocks, For the The adjustment factor for each block of information, is the activation function, is the weight matrix of the text mapping model, For the The embedding vector representation of each information block is: It is the bias term of the text mapping model.

[0045] Step 103, setting a time series dynamic acquisition model for electronic medical records, and acquiring an embedded vector representation of each time point of the information block according to the embedded vector representation of the information block; Specifically, the temporal dynamic acquisition model of electronic medical records includes: , in, For the The embedding vector representation of each time point is Dynamically obtain the model's weight matrix for time series, For the The embedding vector representation of each time point is Electronic Medical Records No. Time series data at a time point, Dynamically obtain the model's bias terms for time series.

[0046] Step 104, setting a disease and symptom prediction model, and predicting the user's disease type based on the global representation of the electronic medical record and the embedded vector representation of each time point; Specifically, the disease and symptom prediction models include: , in, Electronic medical records Determine whether the user has a disease The probability of is the first weight matrix of the disease and symptom prediction model, is the second weight matrix of the disease and symptom prediction model, For the The embedding vector representation of each time point is is the bias term of the disease and symptom prediction model.

[0047] Step 105, based on the prediction results, recommend drugs to the user, and set up an efficacy evaluation model for the recommended drugs to evaluate the efficacy of the drugs after the user takes them.

[0048] Specifically, the recommended drug efficacy evaluation model includes: , in, Electronic medical records The efficacy evaluation value of the recommended drug, For the types of drugs recommended, For the Adjustment factors for recommended drugs of this class, is the weight matrix of the efficacy evaluation model, For the The characteristics of the recommended drugs are shown in Figure 2. is the bias term of the efficacy evaluation model.

[0049] Specifically, a drug efficacy evaluation model is set up to evaluate the efficacy of the drug after the user takes the drug, including: comparing the efficacy evaluation value with a preset evaluation threshold; when the preset evaluation threshold is exceeded, the recommended drug is effective; otherwise, the drug is recommended again.

[0050] Example 4 An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to execute the electronic medical record analysis method based on natural language processing.

[0051] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0052] Among them, the storage medium can be used to store software programs and modules, such as an electronic medical record analysis method based on natural language processing in an embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned electronic medical record analysis method based on natural language processing. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0053] The processor can call the information and application stored in the storage medium through the transmission system to perform the following steps: Step 101, obtain the user's electronic medical record, divide the electronic medical record into multiple information blocks, each of the information blocks corresponds to an element in the electronic medical record, and extract the embedded vector representation of each information block through a natural language model (such as BERT, ChatGPT, etc.), wherein the certain element is: user information, symptoms, medication name, and time taken for recovery; Step 102, setting a text mapping model for the electronic medical record, and obtaining a global representation of the electronic medical record according to the embedded vector representation of the information block; Specifically, the text mapping model of electronic medical records includes: , in, Electronic Medical Records The global representation of is the number of information blocks, For the The adjustment factor for each block of information, is the activation function, is the weight matrix of the text mapping model, For the The embedding vector representation of each information block is: It is the bias term of the text mapping model.

[0054] Step 103, setting a temporal dynamic acquisition model for electronic medical records, and acquiring an embedded vector representation of each time point of the information block according to the embedded vector representation of the information block; Specifically, the temporal dynamic acquisition model of electronic medical records includes: , in, For the The embedding vector representation of each time point is Dynamically obtain the model's weight matrix for time series, For the The embedding vector representation of each time point is Electronic Medical Records No. Time series data at a time point, Dynamically obtain the model's bias terms for time series.

[0055] Step 104, setting a disease and symptom prediction model, and predicting the user's disease type based on the global representation of the electronic medical record and the embedded vector representation of each time point; Specifically, the disease and symptom prediction models include: , in, Electronic medical records Determine whether the user has a disease The probability of is the first weight matrix of the disease and symptom prediction model, is the second weight matrix of the disease and symptom prediction model, For the The embedding vector representation of each time point is is the bias term of the disease and symptom prediction model.

[0056] Step 105, based on the prediction results, recommend drugs to the user, and set up an efficacy evaluation model for the recommended drugs to evaluate the efficacy of the drugs after the user takes them.

[0057] Specifically, the recommended drug efficacy evaluation model includes: , in, Electronic medical records The efficacy evaluation value of the recommended drug, For the types of drugs recommended, For the Adjustment factors for recommended drugs of this class, is the weight matrix of the efficacy evaluation model, For the The characteristics of the recommended drugs are shown in Figure 2. is the bias term of the efficacy evaluation model.

[0058] Specifically, a drug efficacy evaluation model is set up to evaluate the efficacy of the drug after the user takes the drug, including: comparing the efficacy evaluation value with a preset evaluation threshold; when the preset evaluation threshold is exceeded, the recommended drug is effective; otherwise, the drug is recommended again.

[0059] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0060] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0061] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0062] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0063] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0064] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0065] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for analyzing electronic medical records based on natural language processing, characterized in that: include: Obtaining the user's electronic medical record, dividing the electronic medical record into multiple information blocks, each of which corresponds to an element in the electronic medical record, and extracting an embedded vector representation of each of the information blocks through a natural language model, wherein the element is: user information, symptoms, medication name, and time taken for recovery; Setting a text mapping model for the electronic medical record, and obtaining a global representation of the electronic medical record according to the embedded vector representation of the information block; Setting a time series dynamic acquisition model for electronic medical records, and acquiring an embedded vector representation of each time point of the information block according to the embedded vector representation of the information block; Setting a disease and symptom prediction model, and predicting the disease type of the user based on the global representation of the electronic medical record and the embedded vector representation at each time point; Based on the prediction results, drugs are recommended to users, and an efficacy evaluation model for the recommended drugs is set up to evaluate the efficacy of the drugs after users take them.

2. The electronic medical record analysis method based on natural language processing as claimed in claim 1, characterized in that: The text mapping model for electronic medical records includes: ， in, Electronic Medical Records The global representation of is the number of information blocks, For the The adjustment factor for each block of information, is the activation function, is the weight matrix of the text mapping model, For the The embedding vector representation of each information block is: It is the bias term of the text mapping model.

3. The electronic medical record analysis method based on natural language processing as claimed in claim 2, characterized in that: The temporal dynamic acquisition model of electronic medical records includes: ， in, For the The embedding vector representation of each time point is Dynamically obtain the model's weight matrix for time series, For the The embedding vector representation of each time point is Electronic Medical Records No. Time series data at a time point, Dynamically obtain the model's bias terms for time series.

4. The electronic medical record analysis method based on natural language processing as claimed in claim 3, characterized in that: Disease and symptom prediction models include: ， in, Electronic medical records Determine whether the user has a disease The probability of is the first weight matrix of the disease and symptom prediction model, is the second weight matrix of the disease and symptom prediction model, For the The embedding vector representation of each time point is is the bias term of the disease and symptom prediction model.

5. The electronic medical record analysis method based on natural language processing as claimed in claim 4, characterized in that: The recommended drug efficacy evaluation models include: ， in, Electronic medical records The efficacy evaluation value of the recommended drug, For the types of drugs recommended, For the Adjustment factors for recommended drugs of this class, is the weight matrix of the efficacy evaluation model, For the The characteristics of the recommended drugs are shown in Figure 2. is the bias term of the efficacy evaluation model.

6. The electronic medical record analysis method based on natural language processing as claimed in claim 5, characterized in that: Setting up an efficacy evaluation model for recommended drugs and evaluating the efficacy of drugs after users take the drugs includes: comparing the efficacy evaluation value with a preset evaluation threshold; when the preset evaluation threshold is exceeded, the recommended drug is effective; otherwise, the drug is recommended again.

7. An electronic medical record analysis system based on natural language processing, characterized in that: include: An information acquisition module is used to acquire the user's electronic medical record, divide the electronic medical record into multiple information blocks, each of which corresponds to an element in the electronic medical record, and extract an embedded vector representation of each information block through a natural language model, wherein the element is: user information, symptoms, medication name, and time taken for recovery; A text mapping module, used to set a text mapping model for the electronic medical record, and obtain a global representation of the electronic medical record according to the embedded vector representation of the information block; A time series dynamic acquisition module, used to set a time series dynamic acquisition model for electronic medical records, and acquire an embedded vector representation of each time point of the information block according to the embedded vector representation of the information block; A symptom prediction module, used to set a disease and symptom prediction model and predict the disease type of the user based on the global representation of the electronic medical record and the embedded vector representation of each time point; The efficacy evaluation module is used to recommend drugs to users based on the prediction results, set up an efficacy evaluation model for the recommended drugs, and evaluate the efficacy of the drugs after the users take them.

8. The electronic medical record analysis system based on natural language processing as claimed in claim 7, characterized in that: The text mapping model for electronic medical records includes: ， in, Electronic Medical Records The global representation of is the number of information blocks, For the The adjustment factor for each block of information, is the activation function, is the weight matrix of the text mapping model, For the The embedding vector representation of each information block is: It is the bias term of the text mapping model.

9. The electronic medical record analysis system based on natural language processing as claimed in claim 8, characterized in that: The temporal dynamic acquisition model of electronic medical records includes: ， in, For the The embedding vector representation of each time point is Dynamically obtain the model's weight matrix for time series, For the The embedding vector representation of each time point is Electronic Medical Records No. Time series data at a time point, Dynamically obtain the model's bias terms for time series.

10. The electronic medical record analysis system based on natural language processing as claimed in claim 9, characterized in that: Disease and symptom prediction models include: ， in, Electronic medical records Determine whether the user has a disease The probability of is the first weight matrix of the disease and symptom prediction model, is the second weight matrix of the disease and symptom prediction model, For the The embedding vector representation of each time point is is the bias term of the disease and symptom prediction model.

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