Disease data analysis method and device, electronic equipment and storage medium

Abstract

The invention provides a disease data analysis method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source disease data associated with a target region within a preset time period; wherein the multi-source disease data comprises at least one of the following items: drugstore sales data in the target area, school absence conditions, hospital doctor seeing amount data, pathogen data, epidemiological survey reports and disease news information data; constructing an area graph network of the target area based on the multi-source disease data; and splicing the region graph network and the multi-source fusion features of the multi-source disease data, inputting the spliced features into a disease data analysis model containing a plurality of expert agents, and outputting a disease analysis result of the target region.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a disease data analysis method, device, electronic equipment and storage medium. Background Art

[0002] With the continuous advancement of technology, data analysis technology has been widely used in many fields, for example, it can be used for in-depth analysis of disease-related data.

[0003] In the process of disease transmission, there is usually a certain transmission cycle and incubation period, which may lead to a lag in early warning of the disease. However, with the help of effective data analysis methods, it can help identify the risk of disease transmission in advance, thereby buying valuable time for disease prevention and control measures.

[0004] Therefore, how to effectively analyze disease data has become a problem that needs to be solved urgently in the industry. Summary of the invention

[0005] The present invention provides a disease data analysis method, device, electronic device and storage medium to solve the problem of how to effectively perform disease data analysis in the prior art.

[0006] The present invention provides a disease data analysis method, comprising: Acquire multi-source disease data associated with a target area within a preset time period; wherein the multi-source disease data includes at least one of the following: pharmacy sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports, and disease news information data within the target area; Based on the multi-source disease data, construct a regional graph network of the target area; After the regional graph network and the multi-source fusion features of the multi-source disease data are spliced, they are input into a disease data analysis model including multiple expert agents, and the disease analysis results of the target area are output.

[0007] According to a disease data analysis method provided by the present invention, based on the multi-source disease data, a regional graph network of the target area is constructed, comprising: Divide the target area into multiple sub-areas, and determine the edge weights between the sub-areas based on the number of common activity scenes of disease infected persons between the sub-areas and the total number of activity scenes of disease infected persons in the sub-areas in the epidemiological survey report of the multi-source disease data; Different sub-regions are connected based on the edge weights between the sub-regions to construct a regional graph network of the target region.

[0008] According to a disease data analysis method provided by the present invention, after the regional graph network and the multi-source fusion features of the multi-source disease data are spliced, they are input into a disease data analysis model including multiple expert agents, and the disease analysis results of the target area are output, including: After splicing the regional graph network and the multi-source fusion features of the multi-source disease data, the splicing is input into a disease data analysis model including multiple expert agents to obtain disease occurrence possibility information, disease severity information and disease description text output by each expert agent; Determine the prediction result of the disease occurrence possibility of the target area according to the disease occurrence possibility information corresponding to each expert agent and the confidence of the expert agent; Determine the disease severity prediction result of the target area according to the disease severity information corresponding to each expert agent and the expert agent confidence; Determining a disease analysis result of the target area based on the disease occurrence possibility prediction result of the target area and the disease severity prediction result; The expert agent confidence is determined based on the quantitative certainty of the expert agent and the historical prediction accuracy.

[0009] According to a disease data analysis method provided by the present invention, the method for obtaining the disease occurrence possibility prediction result includes: Summing the disease occurrence possibility information of each of the expert agents and the product of the expert agent confidence to obtain a first summation result; Determining the disease occurrence possibility prediction result according to the first summation result and the summation result of the confidence of each of the expert agents; The method for obtaining the disease severity prediction result comprises: Summing the disease severity information of each of the expert agents and the product of the expert agent confidence to obtain a second summation result; The disease severity prediction result is determined based on the sum of the second summation result and the confidence level of each expert agent.

[0010] According to a disease data analysis method provided by the present invention, the method for calculating the confidence of the expert agent includes: Determining the historical accuracy of the expert agent based on the difference between the disease occurrence possibility information output by the expert agent at different time points and the actual disease occurrence possibility information; Determining the quantitative certainty of the expert agent according to the number of uncertainty words, the total number of words, and the number of key entity words in the disease description text output by the expert agent; The expert agent confidence is obtained based on the historical accuracy of the expert agent and the mean of the quantitative certainty.

[0011] According to a disease data analysis method provided by the present invention, after splicing the regional graph network and the multi-source fusion features of the multi-source disease data, the splicing is input into a disease data analysis model including multiple expert agents, and before the step of outputting the disease analysis result of the target region, the method further includes: The spliced ​​regional graph network sample and the multi-source fusion feature sample are used as a training sample to obtain multiple training samples; For each of the training samples, the training samples are used as environmental states and are respectively input into each expert agent in the disease data analysis model to obtain experience data of each of the expert agents, and the experience data are stored in the experience pool corresponding to each of the expert agents; wherein the experience data includes: agent reward value and agent action selection; Calculating the individual advantage of each expert agent according to the agent reward value in the experience pool of each of the expert agents; Calculating the loss value of each expert agent according to the individual advantage and action selection strategy ratio of each expert agent, so as to optimize the policy network parameters of the corresponding expert agent according to the loss value; Each of the training samples is traversed until the preset training conditions are met, thereby obtaining a disease data analysis model including multiple expert agents.

[0012] According to a disease data analysis method provided by the present invention, the step of obtaining multi-source disease data associated with a target area within a preset time period includes: The pharmacy sales data, school absences, and hospital visit volume are standardized by a sliding window standardization method to obtain standardized pharmacy sales data, school absences, and hospital visit volume data; Call the pre-configured large language model to extract key disease entities and relationships from news information data to obtain disease news information data; When the virus sequence data of the disease is obtained, the pre-configured large language model is called to analyze the key sites in the virus sequence data to obtain the pathogen data.

[0013] According to a disease data analysis method provided by the present invention, the multi-source fusion features include: numerical features of numerical data, text features of text data and pathogenic data features of pathogenic data; The numerical data includes at least one of the following: pharmacy sales data, school absence data, and hospital visit data; The text data includes at least one of the following: epidemiological survey reports and disease news information data.

[0014] The present invention provides a disease data analysis device, comprising: An acquisition module, used to acquire multi-source disease data associated with a target area within a preset time period; wherein the multi-source disease data includes at least one of the following: pharmacy sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports, and disease news information data within the target area; A processing module, configured to construct a regional map network of the target area based on the location information of the multi-source disease data; The analysis module is used to splice the regional graph network and the multi-source fusion features of the multi-source disease data, input them into a disease data analysis model including multiple expert agents, and output the disease analysis results of the target area.

[0015] The present invention also provides a disease data analysis device, which is also used for: Divide the target area into multiple sub-areas, and determine the edge weights between the sub-areas based on the number of common activity scenes of disease infected persons between the sub-areas and the total number of activity scenes of disease infected persons in the sub-areas in the epidemiological survey report of the multi-source disease data; Different sub-regions are connected based on the edge weights between the sub-regions to construct a regional graph network of the target region.

[0016] The present invention also provides a disease data analysis device, which is also used for: After splicing the regional graph network and the multi-source fusion features of the multi-source disease data, the splicing is input into a disease data analysis model including multiple expert agents to obtain disease occurrence possibility information, disease severity information and disease description text output by each expert agent; Determine the prediction result of the disease occurrence possibility of the target area according to the disease occurrence possibility information corresponding to each expert agent and the confidence of the expert agent; Determine the disease severity prediction result of the target area according to the disease severity information corresponding to each expert agent and the expert agent confidence; Determining a disease analysis result of the target area based on the disease occurrence possibility prediction result of the target area and the disease severity prediction result; The expert agent confidence is determined based on the quantitative certainty of the expert agent and the historical prediction accuracy.

[0017] The present invention also provides a disease data analysis device, which is also used for: Summing the disease occurrence possibility information of each of the expert agents and the product of the expert agent confidence to obtain a first summation result; Determining the disease occurrence possibility prediction result according to the first summation result and the summation result of the confidence of each of the expert agents; The method for obtaining the disease severity prediction result comprises: Summing the disease severity information of each of the expert agents and the product of the expert agent confidence to obtain a second summation result; The disease severity prediction result is determined based on the sum of the second summation result and the confidence level of each expert agent.

[0018] The present invention also provides a disease data analysis device, which is also used for: Determining the historical accuracy of the expert agent based on the difference between the disease occurrence possibility information output by the expert agent at different time points and the actual disease occurrence possibility information; Determining the quantitative certainty of the expert agent according to the number of uncertainty words, the total number of words, and the number of key entity words in the disease description text output by the expert agent; The expert agent confidence is obtained based on the historical accuracy of the expert agent and the mean of the quantitative certainty.

[0019] The present invention also provides a disease data analysis device, which is also used for: The spliced ​​regional graph network sample and the multi-source fusion feature sample are used as a training sample to obtain multiple training samples; For each of the training samples, the training samples are used as environmental states and are respectively input into each expert agent in the disease data analysis model to obtain experience data of each of the expert agents, and the experience data are stored in the experience pool corresponding to each of the expert agents; wherein the experience data includes: agent reward value and agent action selection; Calculating the individual advantage of each expert agent according to the agent reward value in the experience pool of each of the expert agents; Calculating the loss value of each expert agent according to the individual advantage and action selection strategy ratio of each expert agent, so as to optimize the policy network parameters of the corresponding expert agent according to the loss value; Each of the training samples is traversed until the preset training conditions are met, thereby obtaining a disease data analysis model including multiple expert agents.

[0020] The present invention also provides a disease data analysis device, which is also used for: The pharmacy sales data, school absences, and hospital visit volume are standardized by a sliding window standardization method to obtain standardized pharmacy sales data, school absences, and hospital visit volume data; Call the pre-configured large language model to extract key disease entities and relationships from news information data to obtain disease news information data; When the virus sequence data of the disease is obtained, the pre-configured large language model is called to analyze the key sites in the virus sequence data to obtain the pathogen data.

[0021] According to a disease data analysis device provided by the present invention, the multi-source fusion features include: numerical features of numerical data, text features of text data and pathogenic data features of pathogenic data; The numerical data includes at least one of the following: pharmacy sales data, school absence data, and hospital visit data; The text data includes at least one of the following: epidemiological survey reports and disease news information data.

[0022] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the disease data analysis method described above is implemented.

[0023] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the disease data analysis methods described above.

[0024] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the disease data analysis method described above is implemented.

[0025] The disease data analysis method, device, electronic device and storage medium provided by the present invention can comprehensively reflect the disease-related dynamics in the target area by collecting multi-source disease data such as pharmacy sales data, school absenteeism, and hospital visits. The cross-modal attention mechanism can effectively fuse numerical data and text data. By learning the correlation between different modal data, the model can more accurately capture the key features of disease transmission and improve the accuracy of data analysis. The constructed regional map network uses geographical regions as nodes, which can intuitively display the spread of diseases in different regions. The disease data analysis model containing multiple expert agents can analyze disease data from different angles and ultimately generate accurate and effective disease data analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 It is a flow chart of the disease data analysis method provided by the present invention; Figure 2 A schematic diagram of the structure of the disease data analysis device provided by the present invention; Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Figure 1 is a flow chart of the disease data analysis method provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 110, obtaining multi-source disease data associated with the target area within a preset time period; wherein the multi-source disease data includes at least one of the following: pharmacy sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports, and disease news information data within the target area; In the present invention, the preset time period refers to a time range determined according to specific analysis requirements and is used to collect disease data related to the target area.

[0030] The preset time period is determined by taking the time of the warning signal as the base date when no epidemiological investigation is conducted. When an epidemiological investigation has been conducted, the "time of the first disease infection" in the epidemiological investigation report is taken as the base date. The preset time period can specifically refer to a window period before the base date to ensure the consistency of the data timeline.

[0031] In the present invention, the target area may refer to a specific geographical scope for disease analysis, such as a city, province, country, etc.

[0032] In the present invention, multi-source disease data include various types of data such as drugstore sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports, and disease news information data.

[0033] More specifically, drugstore sales data refers to the drug sales records of drugstores in the target area within a specific time period, which may include sales information of disease-related drugs such as antipyretics and cough suppressants.

[0034] During a disease outbreak, sales of related drugs often increase significantly. Monitoring pharmacy sales data can help detect early signs of disease spread.

[0035] School absences record the number of students absent and the reasons for absences, which comes from school attendance records. The spread of disease among students will lead to an increase in the number of absences, which helps to assist in judging the spread of the disease.

[0036] The hospital visit volume data counts the number of outpatients and inpatients in each department of the hospital, directly reflecting the disease medical burden in the region. An abnormal increase in the number of visits may indicate a disease outbreak.

[0037] The epidemiological investigation report is written by professionals and covers disease transmission routes, case exposure history, close contact information, etc., providing key basis for disease research. The epidemiological investigation report can be obtained from public website information.

[0038] Disease news information data includes news reports, discussion trends about diseases on social media, and other information, which can reflect the public's attention to the disease and assist in monitoring the spread and social impact of the disease. These data are combined to improve the accuracy and reliability of disease analysis.

[0039] Step 120, constructing a regional graph network of the target area based on the multi-source disease data; In the present invention, a regional graph network of a target area is constructed based on multi-source disease data, which is a graph structure that uses geographical areas as spatial units and the association relationship between regions as a connecting link.

[0040] For example, if there are more common activity sites between two regions, it means that the mobility and contact between people in these two regions are more likely, and the risk of disease transmission is relatively high, so the edge weight is larger. The regional graph network can intuitively show the spread of diseases in different regions, providing strong support for the identification of disease transmission paths and the location of high-risk areas.

[0041] Step 130, after splicing the regional graph network and the multi-source fusion features of the multi-source disease data, input them into a disease data analysis model including multiple expert agents, and output the disease analysis results of the target area.

[0042] In the present invention, the structural information of the regional graph network and the multi-source fusion features are spliced ​​to form a unified feature vector. The splicing operation can be a simple vector concatenation, that is, the node features of the regional graph network and the multi-source fusion features are sequentially connected into a long vector. The fused feature vector contains both the structural information of the regional graph network and the key information of the multi-source fusion features, which can comprehensively describe the disease propagation situation in the target area.

[0043] The disease data analysis model contains models of multiple expert agents, each of which focuses on a different disease analysis task.

[0044] For example, some agents are good at predicting disease transmission trends, while others focus on assessing medical resource needs, etc. Each agent has its own strategy network and value network, and can perform independent analysis and judgment based on the input feature vector.

[0045] In an optional embodiment, the disease data analysis model includes: clinical medicine experts, disease prevention and control experts, virology experts, data scientists, medical resource planning experts, etc., which can be specifically configured and selected through a pre-prepared expert agent selection interface.

[0046] The disease data analysis model conducts a comprehensive analysis based on the output results of each expert agent and outputs the final disease analysis results.

[0047] In the present invention, the final disease analysis result may include a prediction result of the likelihood of disease occurrence in the target area and a prediction result of the severity of the disease.

[0048] More specifically, the prediction result of the probability of disease occurrence can refer to the probability of disease outbreak or spread in the target area. It is expressed as a value between 0 and 1, and the higher the value, the greater the possibility of disease occurrence. It helps decision makers understand the risk level of disease outbreak so that they can formulate prevention and response measures in advance.

[0049] The disease severity prediction result is an assessment of the severity that a disease outbreak may cause in the target area. It is expressed as a value between 0 and 1, with a higher value indicating a more severe disease.

[0050] Let decision makers know the potential harm of the disease, allocate medical resources reasonably, and formulate corresponding treatment and isolation plans. Through these specific prediction results, decision makers can carry out disease prevention and control and resource allocation more scientifically, and improve their ability to respond to disease outbreaks.

[0051] In the present invention, by collecting multi-source disease data such as pharmacy sales data, school absences, and hospital visits, the disease-related dynamics in the target area can be fully reflected. The cross-modal attention mechanism can effectively fuse numerical data and text data. By learning the correlation between different modal data, the model can more accurately capture the key features of disease transmission and improve the accuracy of data analysis. The constructed regional graph network uses geographical regions as nodes, which can intuitively display the spread of diseases in different regions. The disease data analysis model containing multiple expert agents can analyze disease data from different angles and ultimately generate accurate and effective disease data analysis results.

[0052] Optionally, the multi-source fusion features include: numerical features of numerical data, text features of text data, and pathogenic data features of pathogenic data; The numerical data includes at least one of the following: pharmacy sales data, school absence data, and hospital visit data; The text data includes at least one of the following: epidemiological survey reports and disease news information data.

[0053] In the present invention, numerical data refers to information that is expressed in numerical form, is quantifiable, and can be subjected to mathematical operations. It is commonly found in multi-source disease data such as pharmacy sales records, school absenteeism statistics, and hospital visit statistics.

[0054] For example, the sales volume of antipyretics and cough suppressants in a pharmacy on a certain day, the number of students absent from a class in a school due to illness, the number of patients seen by a department in a hospital on that day, etc., all provide quantitative indicators for disease analysis.

[0055] Text data is descriptive information recorded in text form, which is commonly found in epidemiological survey reports and disease news information.

[0056] For example, the activity trajectory of cases and symptom descriptions in investigation reports, the disease transmission dynamics and public discussion heat in news reports, etc., can use natural language processing technology to extract key entities and relationships and convert them into structured feature vectors for analysis.

[0057] The pathogen data features of pathogen data focus on the pathogen itself, such as viral gene sequences, bacterial culture results, etc., which are obtained after analyzing key sites and help understand the transmission ability and pathogenicity of pathogens.

[0058] In the present invention, multi-source fusion features integrate different types of disease data into a unified feature representation, providing a more comprehensive information basis for disease analysis.

[0059] Optionally, constructing a regional graph network of the target area based on the multi-source disease data includes: Divide the target area into multiple sub-areas, and determine the edge weights between the sub-areas based on the number of common activity scenes of disease infected persons between the sub-areas and the total number of activity scenes of disease infected persons in the sub-areas in the epidemiological survey report of the multi-source disease data; Different sub-regions are connected based on the edge weights between the sub-regions to construct a regional graph network of the target region.

[0060] In the present invention, a target area (such as a city) is divided into a plurality of sub-areas, for example, streets or communities are used as units.

[0061] Each sub-region is a node, and the feature vector of the node includes the following indicators: Pharmacy sales: sales of antipyretics, cough suppressants and other disease-related medicines in pharmacies within the sub-region; school absenteeism rate: the proportion of absent students in schools within the sub-region; number of medical visits: the number of visits to hospitals and clinics within the sub-region; medical resource utilization rate: the utilization of medical resources (such as beds, equipment, etc.) within the sub-region.

[0062] In the present invention, the number of common activity places refers to the number of common activity places between sub-area i and sub-area j. These places are potential high-risk areas for disease transmission, such as shopping malls, parks, schools, transportation hubs, etc.

[0063] Count the number of common activity venues between sub-region i and sub-region j. These venues are potential high-risk areas for disease transmission, such as shopping malls, parks, schools, transportation hubs, etc. At the same time, count the total number of all activity venues in sub-region i.

[0064] The calculation method of edge weight is as follows: ; In the present invention, based on the above node definition and edge weight calculation, a regional graph network of the target area is constructed.

[0065] Each node represents a sub-region, and the connection strength between nodes is determined by the edge weight. A graph data structure (such as an adjacency matrix or adjacency list) is used to represent the regional graph network, where nodes represent sub-regions and edges represent the strength of association between sub-regions.

[0066] In the present invention, the regional graph network can intuitively display the spread of the disease in different sub-regions, helping to analyze the disease's spread path and high-risk areas. By calculating the edge weights, the regional graph network can accurately reflect the strength of the association between sub-regions and ensure the reasonable alignment of data in the spatial dimension.

[0067] Optionally, after the regional graph network and the multi-source fusion features of the multi-source disease data are spliced, the splicing is input into a disease data analysis model including multiple expert agents, and the disease analysis result of the target region is output, including: After splicing the regional graph network and the multi-source fusion features of the multi-source disease data, the splicing is input into a disease data analysis model including multiple expert agents to obtain disease occurrence possibility information, disease severity information and disease description text output by each expert agent; Determine the prediction result of the disease occurrence possibility of the target area according to the disease occurrence possibility information corresponding to each expert agent and the confidence of the expert agent; Determine the disease severity prediction result of the target area according to the disease severity information corresponding to each expert agent and the expert agent confidence; Determining a disease analysis result of the target area based on the disease occurrence possibility prediction result of the target area and the disease severity prediction result; The expert agent confidence is determined based on the quantitative certainty of the expert agent and the historical prediction accuracy.

[0068] In the present invention, the regional graph network provides spatial information of each sub-region in the target region and their interrelationships. Through splicing, the model can understand the disease transmission path and intensity in different sub-regions, as well as the transmission risk between sub-regions.

[0069] Multi-source fusion features include key information extracted from multiple data sources, such as numerical data such as pharmacy sales, school absenteeism, and medical visits, as well as text data such as epidemiological survey reports and disease news information. This information reflects different aspects of disease transmission.

[0070] By combining the regional graph network with multi-source fusion features, the model can simultaneously use spatial information and multi-source features for comprehensive analysis. This helps the model to more accurately grasp the laws of disease transmission and improve the accuracy of disease probability and severity prediction.

[0071] In the present invention, each expert agent outputs a disease occurrence probability value Pk based on the input feature vector, that is, the spliced ​​regional graph network and multi-source fusion features, which represents the prediction of agent k on the possibility of disease occurrence in the target area.

[0072] The confidence of each expert agent It is determined based on its quantitative certainty and historical prediction accuracy. is the historical prediction accuracy of agent k, calculated by the weighted mean absolute error.

[0073] In the present invention, according to the quantitative certainty It is the quantitative value of the evidence certainty of agent k, which is evaluated based on the word frequency of uncertainty words and the coverage of key entities in the evidence text.

[0074] Prediction of the likelihood of disease occurrence and disease severity prediction results Perform geometric averaging to obtain a comprehensive risk assessment index Final_Probability,

[0075] In the present invention, the outbreak possibility and severity of the disease are combined through the geometric mean method to obtain a comprehensive disease risk assessment, providing more powerful support for decision makers.

[0076] Optionally, the method for obtaining the disease occurrence possibility prediction result includes: Summing the disease occurrence possibility information of each of the expert agents and the product of the expert agent confidence to obtain a first summation result; Determining the disease occurrence possibility prediction result according to the first summation result and the summation result of the confidence of each of the expert agents; The method for obtaining the disease severity prediction result comprises: Summing the disease severity information of each of the expert agents and the product of the expert agent confidence to obtain a second summation result; The disease severity prediction result is determined based on the sum of the second summation result and the confidence level of each expert agent.

[0077] In the present invention, the disease occurrence probability information of all expert agents is calculated The corresponding confidence The sum of the products, i.e. the first summation result , summarizes the importance of each agent’s prediction results, and agents with higher confidence have a greater impact on the total.

[0078] More specifically, the confidence of all expert agents is calculated The sum of , which is used to normalize the sum of the numerator parts to ensure that the final result is within a reasonable numerical range.

[0079] By dividing the numerator by the denominator, we can get the predicted result of the disease occurrence probability in the target area: ; In the present invention, the disease severity information of all expert agents is calculated The corresponding confidence The sum of the products, i.e. the second summation result , summarizes the importance of each agent’s prediction results, and agents with higher confidence have a greater impact on the total.

[0080] More specifically, the confidence of all expert agents is calculated The sum of , which is used to normalize the sum of the numerator parts to ensure that the final result is within a reasonable numerical range.

[0081] By dividing the numerator by the denominator, we can get the predicted result of the disease occurrence probability in the target area: ; In the present invention, by weighting and integrating the prediction results of multiple expert agents, the expertise and perspectives of different agents can be comprehensively considered to avoid the prediction results of a single agent being too one-sided or affected by its limitations. In addition, agents with high confidence contribute more to the final prediction results, which enables the prediction results of agents with high historical prediction accuracy and strong evidence certainty to be more prominently reflected in the final prediction of the likelihood and severity of the disease. By weighting the prediction results using the confidence of the expert agent, it is possible to filter out prediction information with low accuracy to a certain extent, reduce errors, and thus improve the accuracy and reliability of the entire prediction system.

[0082] Optionally, the method for calculating the confidence of the expert agent includes: Determining the historical accuracy of the expert agent based on the difference between the disease occurrence possibility information output by the expert agent at different time points and the actual disease occurrence possibility information; Determining the quantitative certainty of the expert agent according to the number of uncertainty words, the total number of words, and the number of key entity words in the disease description text output by the expert agent; The expert agent confidence is obtained based on the historical accuracy of the expert agent and the mean of the quantitative certainty.

[0083] In the present invention, the disease occurrence possibility information is the disease occurrence probability predicted by the agent, usually expressed as a value between 0 and 1. The actual disease occurrence possibility information is the actual disease occurrence probability obtained based on subsequent data or statistics, which is used to compare the prediction accuracy of the agent.

[0084] In the disease description text output by the agent, the number of uncertainty words refers to the number of times words such as "maybe" and "perhaps" appear, which reflect the uncertainty of the prediction results.

[0085] The total number of words is the total number of all words in the text and is used to calculate the proportion of uncertain words.

[0086] The number of key entity words refers to important disease-related terms in the text, such as "fever" and "cough". The number of these words can reflect the richness and relevance of text information.

[0087] By comprehensively considering the deviation between the agent's historical predictions and actual results (historical accuracy), as well as the uncertainty of the words and key entity words in the output text (quantitative certainty), the confidence information of each expert agent can be calculated. This comprehensive evaluation method helps to screen out more reliable and accurate agent prediction results, thereby improving the prediction performance of the entire system.

[0088] Specifically, the confidence of each expert agent It is determined based on its quantitative certainty and historical prediction accuracy. is the historical prediction accuracy of agent k, calculated by weighted mean absolute error (WMAE).

[0089]

[0090] in, Expert Agent k In time t The prediction results, refers to the actual results, is the time decay weight.

[0091] In the present invention, according to the quantitative certainty It is the quantitative value of the evidence certainty of agent k, which is evaluated based on the word frequency of uncertainty words and the coverage of key entities in the evidence text.

[0092]

[0093] in, Words that express uncertainty, Indicates the number of key entities, Indicates the total number of words in the evidence text.

[0094] The confidence of each expert agent It is determined based on its quantitative certainty and historical prediction accuracy, and the formula is as follows:

[0095] In the present invention, the mean of historical accuracy and quantitative certainty is used as the confidence information of the intelligent agent. A comprehensive credibility evaluation can be given based on both the intelligent agent's historical prediction performance and the quality of the output text. This not only takes into account the intelligent agent's past prediction accuracy, but also takes into account the certainty and information richness of its output results, making the confidence information more comprehensive and reliable.

[0096] Optionally, after splicing the regional graph network and the multi-source fusion features of the multi-source disease data, inputting them into a disease data analysis model comprising a plurality of expert agents, and before outputting the disease analysis result of the target region, the method further comprises: The spliced ​​regional graph network sample and the multi-source fusion feature sample are used as a training sample to obtain multiple training samples; For each of the training samples, the training samples are used as environmental states and are respectively input into each expert agent in the disease data analysis model to obtain experience data of each of the expert agents, and the experience data are stored in the experience pool corresponding to each of the expert agents; wherein the experience data includes: agent reward value and agent action selection; Calculating the individual advantage of each expert agent according to the agent reward value in the experience pool of each of the expert agents; Calculating the loss value of each expert agent according to the individual advantage and action selection strategy ratio of each expert agent, so as to optimize the policy network parameters of the corresponding expert agent according to the loss value; Each of the training samples is traversed until the preset training conditions are met, thereby obtaining a disease data analysis model including multiple expert agents.

[0097] In the present invention, the regional graph network is based on the sub-regions of the target region as nodes, and the node characteristics include drugstore sales, school absenteeism, number of medical visits, medical resource utilization, etc. The edge weight is determined based on the ratio of the number of common activity venues between sub-regions to the total number of activity venues in a single sub-region.

[0098] The multi-source fusion feature is a feature vector obtained by fusing numerical data, such as pharmacy sales data and hospital visit volume, with text data, such as epidemiological survey reports and disease news information, through a cross-modal attention mechanism. The feature vector of the regional graph network and the multi-source fusion feature vector are sequentially connected into a unified feature vector.

[0099] In the present invention, the experience data includes the agent's reward value and action selection. The reward value is determined based on the degree of match between the agent's output and the actual result, and the action selection is the action taken by the agent under a given environmental state, such as the predicted probability and severity of a disease.

[0100] In the present invention, individual advantage refers to a measure of the advantage of an agent taking a specific action under a specific environmental state, which is obtained by calculating the difference between the agent's reward value and the long-term return expectation estimated by the value network.

[0101] In the present invention, the loss value can be calculated based on the individual advantage and action selection strategy ratio of the agent, and is used to evaluate the quality of the agent's strategy.

[0102] Policy network parameter optimization uses optimization algorithms such as gradient descent to adjust the agent's policy network parameters according to the loss value to improve the agent's decision-making ability.

[0103] More specifically, in the present invention, a domain expert agent can be first constructed, and each agent k (such as Epi-Agent, Pathogen-Agent) independently generates a trajectory ,in: is the environmental state faced by agent k (i.e., multi-channel fusion information at a certain moment); is the judgment result of agent k at time t (such as event level and disposal suggestions); The reward of agent k at time t; Construct an individual advantage value evaluation system to evaluate the independent strategy performance of each agent. The evaluation needs to calculate the cumulative decision-making-reward and punishment value of a single agent in the whole process. The formula is: ; in, is the reward or penalty value of a certain disease at a certain time. is the discount factor (default 0.99); The value network of agent k, used to estimate the state long-term return expectations.

[0104] In the present invention, the spliced ​​regional graph network sample and the multi-source fusion feature sample are used as a training sample to obtain multiple training samples.

[0105] For each training sample, it is input as the environment state to each expert agent in the disease data analysis model. The agent selects actions based on the current strategy, such as predicting the likelihood and severity of the disease, and receives a reward value based on the degree of match between its output and the actual result. This experience data, including the agent reward value and action selection, is collected and stored in the experience pool of the corresponding expert agent.

[0106] Using the reward value in the experience pool, calculate the individual advantage of each expert agent according to the individual advantage formula above. Calculate the loss value and optimize the policy network parameters: Calculate the loss value of each agent based on the individual advantage of each agent and the action selection strategy ratio, that is, the ratio of the probability of action selection of the current strategy to that of the old strategy. Use optimization algorithms, such as gradient descent, to adjust the policy network parameters of the corresponding agent according to the loss value to improve the decision quality.

[0107] Repeat the above steps and traverse all training samples until the preset training conditions are met, such as the loss value convergence or the maximum number of training rounds is reached, and finally a disease data analysis model containing multiple expert agents is obtained.

[0108] In the present invention, the training method can effectively improve the credibility and accuracy of the output results of the intelligent agent. By repeatedly training with multiple training samples, the intelligent agent optimizes the policy network parameters in continuous learning, thereby performing better in disease data analysis. This method ensures that the disease data analysis model can output reliable prediction results of the probability and severity of disease occurrence based on high-quality training data and optimized policy parameters. This provides strong support for the early detection of diseases, the formulation of prevention and control strategies, and the rational allocation of resources.

[0109] Optionally, the acquiring of multi-source disease data associated with the target area within a preset time period includes: The pharmacy sales data, school absences, and hospital visit volume are standardized by a sliding window standardization method to obtain standardized pharmacy sales data, school absences, and hospital visit volume data; Call the pre-configured large language model to extract key disease entities and relationships from news information data to obtain disease news information data; When the virus sequence data of the disease is obtained, the pre-configured large language model is called to analyze the key sites in the virus sequence data to obtain the pathogen data.

[0110] In the present invention, sliding window normalization is a data normalization technique that normalizes data by calculating the mean and standard deviation within a window, and is used to eliminate periodic fluctuations and dimensional differences in data, making the data more stable and suitable for subsequent analysis.

[0111] The Big Language Model is a natural language processing model based on deep learning. After being trained with a large amount of text data, it can understand and generate natural language text. It is used to extract key entities and relationships from text data and convert unstructured text information into structured feature vectors.

[0112] Virus sequence data analysis uses bioinformatics methods to analyze virus sequence data and identify key sites, helping to understand the virus's ability to spread and pathogenicity, and providing a basis for disease prevention and control.

[0113] Specifically, obtain the drugstore sales data, school absence data, hospital visit data, and news information data in the target area within a preset time period, and use sliding window standardization:

[0114] in, is the mean value in the window [tw, t], is the standard deviation, w is the window interval, which is related to the disease type and adapts to periodic fluctuations (such as W = 7 days for respiratory tract). For the data after standardization, Indicated in t Data at the moment.

[0115] In an optional embodiment, for the time series data of school absences, the weekend effect needs to be eliminated: the daily average absence rate (Assuming 5 teaching days per week).

[0116] In the present invention, news information data is input into a preconfigured large language model, and the natural language processing capability of the model is used to extract key disease entities (such as symptoms, transmission pathways) and relationships in the text. The extracted key entities and relationships are converted into structured feature vectors.

[0117] In an optional embodiment, the virus sequence data is input into a preconfigured bioinformatics model. The model is used to analyze key sites in the virus sequence to identify important sites related to disease transmission and pathogenicity. The analysis results are converted into structured pathogen data feature vectors.

[0118] In the present invention, by processing and converting multi-source disease data, high-quality input features are effectively provided for the disease data analysis model.

[0119] The disease data analysis device provided by the present invention is described below. The disease data analysis device described below and the disease data analysis method described above can be referenced to each other.

[0120] Figure 2 The structural diagram of the disease data analysis device provided by the present invention is as follows: Figure 2 As shown, including: The acquisition module 210 is used to acquire multi-source disease data associated with the target area within a preset time period; wherein the multi-source disease data includes at least one of the following: pharmacy sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports, and disease news information data in the target area; The processing module 220 is used to construct a regional map network of the target area based on the location information of the multi-source disease data; The analysis module 230 is used to splice the regional graph network and the multi-source fusion features of the multi-source disease data, input them into a disease data analysis model including multiple expert agents, and output the disease analysis results of the target area.

[0121] The present invention also provides a disease data analysis device, which is also used for: Mapping the numerical data to the query space through a first weight matrix to obtain a query vector, and mapping the text data to the key space through a second weight matrix to obtain a key vector; Determine a normalized weighted attention according to the query vector and the key vector, and perform weighted summation on the text features of the text data according to the weighted attention to obtain a fused text feature representation; The fused text feature representation, the numerical feature representation of the numerical data, and the pathogen data feature representation of the pathogen data are spliced ​​to obtain a multi-source fusion feature.

[0122] The present invention also provides a disease data analysis device, which is also used for: Divide the target area into multiple sub-areas, and determine the edge weights between the sub-areas based on the number of common activity scenes of disease infected persons between the sub-areas and the total number of activity scenes of disease infected persons in the sub-areas in the epidemiological survey report of the multi-source disease data; Different sub-regions are connected based on the edge weights between the sub-regions to construct a regional graph network of the target region.

[0123] The present invention also provides a disease data analysis device, which is also used for: After splicing the regional graph network and the multi-source fusion features of the multi-source disease data, the splicing is input into a disease data analysis model including multiple expert agents to obtain disease occurrence possibility information, disease severity information and disease description text output by each expert agent; Determine the prediction result of the disease occurrence possibility of the target area according to the disease occurrence possibility information corresponding to each expert agent and the confidence of the expert agent; Determine the disease severity prediction result of the target area according to the disease severity information corresponding to each expert agent and the expert agent confidence; Determining a disease analysis result of the target area based on the disease occurrence possibility prediction result of the target area and the disease severity prediction result; The expert agent confidence is determined based on the quantitative certainty of the expert agent and the historical prediction accuracy.

[0124] The present invention also provides a disease data analysis device, which is also used for: Summing the disease occurrence possibility information of each of the expert agents and the product of the expert agent confidence to obtain a first summation result; Determining the disease occurrence possibility prediction result according to the first summation result and the summation result of the confidence of each of the expert agents; The method for obtaining the disease severity prediction result comprises: Summing the disease severity information of each of the expert agents and the product of the expert agent confidence to obtain a second summation result; The disease severity prediction result is determined based on the sum of the second summation result and the confidence level of each expert agent.

[0125] The present invention also provides a disease data analysis device, which is also used for: Determining the historical accuracy of the expert agent based on the difference between the disease occurrence possibility information output by the expert agent at different time points and the actual disease occurrence possibility information; Determining the quantitative certainty of the expert agent according to the number of uncertainty words, the total number of words, and the number of key entity words in the disease description text output by the expert agent; The expert agent confidence is obtained based on the historical accuracy of the expert agent and the mean of the quantitative certainty.

[0126] The present invention also provides a disease data analysis device, which is also used for: The spliced ​​regional graph network sample and the multi-source fusion feature sample are used as a training sample to obtain multiple training samples; For each of the training samples, the training samples are used as environmental states and are respectively input into each expert agent in the disease data analysis model to obtain experience data of each of the expert agents, and the experience data are stored in the experience pool corresponding to each of the expert agents; wherein the experience data includes: agent reward value and agent action selection; Calculating the individual advantage of each expert agent according to the agent reward value in the experience pool of each of the expert agents; Calculating the loss value of each expert agent according to the individual advantage and action selection strategy ratio of each expert agent, so as to optimize the policy network parameters of the corresponding expert agent according to the loss value; Each of the training samples is traversed until the preset training conditions are met, thereby obtaining a disease data analysis model including multiple expert agents.

[0127] The present invention also provides a disease data analysis device, which is also used for: The pharmacy sales data, school absences, and hospital visit volume are standardized by a sliding window standardization method to obtain standardized pharmacy sales data, school absences, and hospital visit volume data; Call the pre-configured large language model to extract key disease entities and relationships from news information data to obtain disease news information data; When the virus sequence data of the disease is obtained, the pre-configured large language model is called to analyze the key sites in the virus sequence data to obtain the pathogen data.

[0128] In the present invention, by collecting multi-source disease data such as pharmacy sales data, school absences, and hospital visits, the disease-related dynamics in the target area can be fully reflected. The cross-modal attention mechanism can effectively fuse numerical data and text data. By learning the correlation between different modal data, the model can more accurately capture the key features of disease transmission and improve the accuracy of data analysis. The constructed regional graph network uses geographical regions as nodes, which can intuitively display the spread of diseases in different regions. The disease data analysis model containing multiple expert agents can analyze disease data from different angles and ultimately generate accurate and effective disease data analysis results.

[0129] Figure 3 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 3As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330 and a communications bus 340, wherein the processor 310, the communications interface 320 and the memory 330 communicate with each other through the communications bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the disease data analysis method, which includes: obtaining multi-source disease data associated with a target area within a preset time period; wherein the multi-source disease data includes at least one of the following: pharmacy sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports and disease news information data in the target area; Based on the multi-source disease data, construct a regional graph network of the target area; After the regional graph network and the multi-source fusion features of the multi-source disease data are spliced, they are input into a disease data analysis model including multiple expert agents, and the disease analysis results of the target area are output.

[0130] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the 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, a server, or a 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, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0131] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the disease data analysis method provided by the above methods, the method comprising: obtaining multi-source disease data associated with a target area within a preset time period; wherein the multi-source disease data includes at least one of the following: pharmacy sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports and disease news information data in the target area; Based on the multi-source disease data, construct a regional graph network of the target area; After the regional graph network and the multi-source fusion features of the multi-source disease data are spliced, they are input into a disease data analysis model including multiple expert agents, and the disease analysis results of the target area are output.

[0132] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when the computer program is executed by a processor to perform the disease data analysis method provided by the above methods, the method comprising: obtaining multi-source disease data associated with a target area within a preset time period; wherein the multi-source disease data comprises at least one of the following: pharmacy sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports, and disease news information data within the target area; Based on the multi-source disease data, construct a regional graph network of the target area; After the regional graph network and the multi-source fusion features of the multi-source disease data are spliced, they are input into a disease data analysis model including multiple expert agents, and the disease analysis results of the target area are output.

[0133] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0134] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A disease data analysis method, characterized in that: include: Acquire multi-source disease data associated with a target area within a preset time period; wherein the multi-source disease data includes at least one of the following: pharmacy sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports, and disease news information data within the target area; Based on the multi-source disease data, construct a regional graph network of the target area; After the regional graph network and the multi-source fusion features of the multi-source disease data are spliced, they are input into a disease data analysis model including multiple expert agents, and the disease analysis results of the target area are output.

2. The disease data analysis method according to claim 1, characterized in that: Based on the multi-source disease data, constructing a regional graph network of the target area, including: Divide the target area into multiple sub-areas, and determine the edge weights between the sub-areas based on the number of common activity scenes of disease infected persons between the sub-areas and the total number of activity scenes of disease infected persons in the sub-areas in the epidemiological survey report of the multi-source disease data; Different sub-regions are connected based on the edge weights between the sub-regions to construct a regional graph network of the target region.

3. The disease data analysis method according to claim 1, characterized in that: After the regional graph network and the multi-source fusion features of the multi-source disease data are spliced, they are input into a disease data analysis model including multiple expert agents, and the disease analysis results of the target region are output, including: After splicing the regional graph network and the multi-source fusion features of the multi-source disease data, the splicing is input into a disease data analysis model including multiple expert agents to obtain disease occurrence possibility information, disease severity information and disease description text output by each expert agent; Determine the prediction result of the disease occurrence possibility of the target area according to the disease occurrence possibility information corresponding to each expert agent and the confidence of the expert agent; Determine the disease severity prediction result of the target area according to the disease severity information corresponding to each expert agent and the expert agent confidence; Determining a disease analysis result of the target area based on the disease occurrence possibility prediction result of the target area and the disease severity prediction result; The expert agent confidence is determined based on the quantitative certainty of the expert agent and the historical prediction accuracy.

4. The disease data analysis method according to claim 3, characterized in that: The method for obtaining the disease occurrence possibility prediction result includes: Summing the disease occurrence possibility information of each of the expert agents and the product of the expert agent confidence to obtain a first summation result; Determining the disease occurrence possibility prediction result according to the first summation result and the summation result of the confidence of each of the expert agents; The method for obtaining the disease severity prediction result comprises: Summing the disease severity information of each of the expert agents and the product of the expert agent confidence to obtain a second summation result; The disease severity prediction result is determined based on the sum of the second summation result and the confidence level of each expert agent.

5. The disease data analysis method according to claim 3, characterized in that: The method for calculating the confidence of the expert agent includes: Determining the historical accuracy of the expert agent based on the difference between the disease occurrence possibility information output by the expert agent at different time points and the actual disease occurrence possibility information; Determining the quantitative certainty of the expert agent according to the number of uncertainty words, the total number of words, and the number of key entity words in the disease description text output by the expert agent; The expert agent confidence is obtained based on the historical accuracy of the expert agent and the mean of the quantitative certainty.

6. The disease data analysis method according to claim 1, characterized in that: After splicing the region graph network and the multi-source fusion features of the multi-source disease data, the splicing is input into a disease data analysis model including a plurality of expert agents, and before the step of outputting the disease analysis result of the target region, the method further comprises: The spliced ​​regional graph network sample and the multi-source fusion feature sample are used as a training sample to obtain multiple training samples; For each of the training samples, the training samples are used as environmental states and are respectively input into each expert agent in the disease data analysis model to obtain experience data of each of the expert agents, and the experience data are stored in the experience pool corresponding to each of the expert agents; wherein the experience data includes: agent reward value and agent action selection; Calculating the individual advantage of each expert agent according to the agent reward value in the experience pool of each of the expert agents; Calculating the loss value of each expert agent according to the individual advantage and action selection strategy ratio of each expert agent, so as to optimize the policy network parameters of the corresponding expert agent according to the loss value; Each of the training samples is traversed until the preset training conditions are met, thereby obtaining a disease data analysis model including multiple expert agents.

7. The disease data analysis method according to claim 1, characterized in that: The step of obtaining multi-source disease data associated with the target area within a preset time period includes: The pharmacy sales data, school absences, and hospital visit volume are standardized by a sliding window standardization method to obtain standardized pharmacy sales data, school absences, and hospital visit volume data; Call the pre-configured large language model to extract key disease entities and relationships from news information data to obtain disease news information data; When the virus sequence data of the disease is obtained, the pre-configured large language model is called to analyze the key sites in the virus sequence data to obtain the pathogen data.

8. The disease data analysis method according to claim 1, characterized in that: The multi-source fusion features include: numerical features of numerical data, text features of text data, and pathogenic data features of pathogenic data; The numerical data includes at least one of the following: pharmacy sales data, school absence data, and hospital visit data; The text data includes at least one of the following: epidemiological survey reports and disease news information data.

9. A disease data analysis device, characterized in that: include: An acquisition module, used to acquire multi-source disease data associated with a target area within a preset time period; wherein the multi-source disease data includes at least one of the following: pharmacy sales data, school absences, hospital visit data, pathogen data, epidemiological survey reports, and disease news information data within the target area; A processing module, configured to construct a regional map network of the target area based on the location information of the multi-source disease data; The analysis module is used to splice the regional graph network and the multi-source fusion features of the multi-source disease data, input them into a disease data analysis model including multiple expert agents, and output the disease analysis results of the target area.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the disease data analysis method according to any one of claims 1 to 8 is implemented.

11. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the disease data analysis method according to any one of claims 1 to 8 is implemented.

Images