Infectious disease risk early warning data processing method and related device

Abstract

The invention discloses an infectious disease risk early warning data processing method, an infectious disease risk early warning data processing device, an infectious disease risk early warning data processing device and a computer readable storage medium, and the method comprises the steps: carrying out the registration processing of a corresponding initial data source based on a received data source parameter, and obtaining a data source; performing data acquisition from the data source based on the corresponding data source configuration to obtain original infectious disease data; performing data processing on the original infectious disease data through an infectious disease model rule to obtain infectious disease basic data; performing data integration and data analysis processing based on the infectious disease basic data and the etiological characteristic data to obtain infectious disease thematic database data corresponding to each infectious disease data type; performing infectious disease monitoring based on all the infectious disease thematic database data and the doctor seeing condition monitoring data to obtain a monitoring result; and performing early warning processing based on the monitoring result.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and more specifically, relates to a method for processing risk warning data of infectious diseases, a device for processing risk warning data of infectious diseases, a risk warning data processing equipment, and a computer-readable storage medium. Background Art

[0002] Infectious disease monitoring and early warning are important basic tasks for disease prevention and control. my country's infectious disease monitoring and early warning work has gone through three stages: manual epidemic reporting, electronicization, and informatization, and has played an important role in different historical periods. The infectious disease online direct reporting system launched in 2004 and the infectious disease automatic early warning system launched in 2008 have achieved the combination of infectious disease monitoring information and early warning technology, and to a certain extent solved the problems of infectious disease reporting and early monitoring and early warning.

[0003] Among the related technologies, the infectious disease online direct reporting system has not yet realized the automated collection of monitoring information. It is a passive monitoring system that mainly reports on known diseases and is highly dependent on the reporting awareness of responsible reporting units and doctors. There are monitoring loopholes and lags in the perception of infectious disease epidemics. In addition, the data basis for early warning is mainly based on the individual case information of infectious diseases diagnosed and reported by medical institutions. It lacks other channels such as syndromes, tests, medication, visits to key departments, absences from school due to illness, public opinion and other infectious disease-related data. The early warning model and technology are not intelligent enough.

[0004] Therefore, how to improve the accuracy and effectiveness of infectious disease monitoring and avoid delayed data reporting is a key issue that technical personnel in this field are concerned about. Summary of the invention

[0005] The purpose of this application is to provide a risk warning data processing method for infectious diseases, a risk warning data processing device for infectious diseases, a risk warning data processing equipment, and a computer-readable storage medium. This method improves the accuracy and effectiveness of infectious disease monitoring and avoids the problem of delayed data reporting.

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a method for processing risk warning data of infectious diseases, comprising:

[0007] Registering the corresponding initial data source based on the received data source parameters to obtain the data source;

[0008] Acquire data from the data source based on the corresponding data source configuration to obtain original infectious disease data;

[0009] Processing the original infectious disease data using infectious disease model rules to obtain basic infectious disease data;

[0010] Perform data integration and data analysis based on the basic infectious disease data and etiological characteristic data to obtain infectious disease subject database data corresponding to each infectious disease data type;

[0011] Conduct infectious disease monitoring based on all the infectious disease database data and medical condition monitoring data to obtain monitoring results;

[0012] Perform early warning processing based on the monitoring results.

[0013] Optionally, registering the corresponding initial data source based on the received data source parameters to obtain the data source includes:

[0014] Based on the received data source parameters, determine the data source, data scope, data connection method, data connection frequency, daily data volume assessment, historical data volume assessment, and data usage, and use them as registration parameters;

[0015] The initial data source is registered based on the registration parameters to obtain a data source.

[0016] Optionally, acquiring data from the data source based on the corresponding data source configuration to obtain original infectious disease data includes:

[0017] Building a connection relationship between the data source and the local database based on the data source configuration;

[0018] Data is acquired based on the connection relationship to obtain the original infectious disease data.

[0019] Optionally, data integration and data analysis are performed based on the basic infectious disease data and etiological characteristic data to obtain infectious disease subject database data corresponding to each infectious disease data type, including:

[0020] Based on big data analysis tools and statistical characteristics of etiological data, the basic data of infectious diseases are integrated and analyzed to obtain infectious disease subject database data corresponding to each type of infectious disease data.

[0021] Optionally, infectious disease monitoring is performed based on all of the infectious disease database data and medical condition monitoring data to obtain monitoring results, including:

[0022] Performing statistical analysis on the infectious disease database data based on spatial distribution, personal characteristic distribution, and time distribution to obtain infectious disease statistical data;

[0023] By obtaining the data of outpatient and inpatient visits of hospitals and institutions and analyzing them, we can get the medical situation;

[0024] By using the national network reporting card and the integrated platform, the statistical data of the infectious diseases and the medical treatment conditions are compared and processed to obtain the comparison results;

[0025] Calculating a comprehensive consistency rate based on the comparison results;

[0026] When the comprehensive consistency rate is greater than a preset value, the infectious disease statistical data and the medical treatment situation are inferred based on the infectious disease risk warning model to obtain a risk monitoring result.

[0027] Optionally, the infectious disease statistical data and the medical treatment situation are inferred based on the infectious disease risk early warning model to obtain risk monitoring results, including:

[0028] The initial model is trained based on the infectious disease statistical training data and the medical condition training data to obtain the infectious disease risk warning model;

[0029] The infectious disease statistical data and the medical treatment situation are inferred based on the infectious disease risk early warning model to obtain the risk monitoring result.

[0030] Optionally, performing early warning processing based on the monitoring result includes:

[0031] When the monitoring results include data on abnormal development of infectious diseases, an early warning message is sent.

[0032] The present application also provides a risk warning data processing device for infectious diseases, comprising:

[0033] A data source registration module is used to register the corresponding initial data source based on the received data source parameters to obtain the data source;

[0034] A data acquisition module, used to acquire data from the data source based on the corresponding data source configuration to obtain original infectious disease data;

[0035] A data processing module is used to process the original infectious disease data using infectious disease model rules to obtain basic infectious disease data;

[0036] A data integration processing module is used to perform data integration and data analysis based on the basic data of infectious diseases and the etiological characteristic data to obtain infectious disease subject database data corresponding to each type of infectious disease data;

[0037] An infectious disease data monitoring module is used to monitor infectious diseases based on all the infectious disease database data and medical condition monitoring data to obtain monitoring results;

[0038] The monitoring and early warning module is used to perform early warning processing based on the monitoring results.

[0039] The present application also provides a risk warning data processing device, including:

[0040] Memory for storing computer programs;

[0041] A processor is used to implement the steps of the risk warning data processing method as described above when executing the computer program.

[0042] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the risk warning data processing method as described above are implemented.

[0043] The present application provides a method for processing risk warning data for infectious diseases, including: registering a corresponding initial data source based on received data source parameters to obtain a data source; acquiring data from the data source based on a corresponding data source configuration to obtain original infectious disease data; processing the original infectious disease data through infectious disease model rules to obtain basic infectious disease data; integrating and analyzing data based on the basic infectious disease data and pathogenic characteristic data to obtain infectious disease subject library data corresponding to each infectious disease data type; monitoring infectious diseases based on all the infectious disease subject library data and medical condition monitoring data to obtain monitoring results; and performing warning processing based on the monitoring results.

[0044] It has the following beneficial effects:

[0045] By registering the data source, we can standardize data source management, ensure the accuracy and completeness of the data, and provide a reliable data source for subsequent processing. Then, we can obtain the original infectious disease data according to the data source configuration, and obtain the required materials efficiently and accurately. By processing data using infectious disease model rules, we can transform the original data into more valuable basic infectious disease data, laying a good foundation for subsequent integrated analysis. By combining the basic infectious disease data and the etiological characteristic data for processing, we can obtain thematic database data for each type of infectious disease data, providing more accurate support for monitoring. By integrating the infectious disease thematic database data and the medical condition monitoring data for monitoring, we can comprehensively and accurately monitor the infectious disease situation and improve the reliability of the monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0047] Figure 1 A flowchart of a method for processing risk warning data of infectious diseases provided in an embodiment of the present application;

[0048] Figure 2 A flowchart of another infectious disease risk warning data processing method provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram comparing the State Grid reporting card and the integrated platform provided in the embodiment of the present application;

[0050] Figure 4 A schematic diagram of the structure of a risk warning data processing device for infectious diseases provided in an embodiment of the present application;

[0051] Figure 5 A schematic diagram of the structure of the risk warning data processing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The purpose of this application is to provide a risk warning data processing method for infectious diseases, a risk warning data processing device for infectious diseases, a risk warning data processing equipment, and a computer-readable storage medium. This method improves the accuracy and effectiveness of infectious disease monitoring and avoids the problem of delayed data reporting.

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0054] The following is an example of an embodiment to illustrate a method for processing risk warning data of an infectious disease provided by the present application.

[0055] Please refer to Figure 1 , Figure 1 A flowchart of a method for processing risk warning data of an infectious disease provided in an embodiment of the present application.

[0056] In this embodiment, the method may include:

[0057] S001, registering the corresponding initial data source based on the received data source parameters to obtain the data source;

[0058] In this step, the relevant characteristics and information of the data source are determined through analysis and processing of data source parameters for accurate registration. These parameters include data source, scope, connection method, frequency, daily and historical data volume assessment, and purpose, covering the key attributes of the data source and providing a basis for subsequent data acquisition and processing.

[0059] Furthermore, this step may include:

[0060] Step 1: Based on the received data source parameters, determine the data source, data range, data connection method, data connection frequency, daily data volume assessment, historical data volume assessment, and data usage, and use them as registration parameters;

[0061] Step 2: Register the initial data source based on the registration parameters to obtain the data source.

[0062] This step can standardize the management of data sources, ensure the accuracy and completeness of data, and provide a reliable data source for subsequent infectious disease data processing.

[0063] S002, acquiring data from a data source based on a corresponding data source configuration to obtain original infectious disease data;

[0064] Based on S001, this step extracts the required data from the registered data source according to the connection relationship constructed by the data source configuration. The data source configuration clarifies the connection method and related settings between the data source and the local database, so that the data acquisition process can be carried out according to the predetermined rules.

[0065] It can be seen that this step can efficiently and accurately obtain original infectious disease data and provide materials for subsequent data processing.

[0066] Optionally, this step may include:

[0067] Step 1: Building a connection relationship between the data source and the local database based on the data source configuration;

[0068] Step 2: Acquire data based on the connection relationship to obtain original infectious disease data.

[0069] S003, process the original infectious disease data through infectious disease model rules to obtain basic infectious disease data;

[0070] Based on S002, this step uses the infectious disease model rules to analyze, screen, calculate and other operations on the original data, extract valuable information, and convert it into basic infectious disease data. These rules are formulated based on relevant knowledge and data characteristics of infectious diseases and can effectively process the original data.

[0071] This step can convert the original data into more valuable basic data on infectious diseases, providing a better foundation for subsequent data integration and analysis.

[0072] S004, perform data integration and data analysis based on the basic data of infectious diseases and the data of etiological characteristics, and obtain the infectious disease subject database data corresponding to each type of infectious disease data;

[0073] Based on S003, this step combines the basic data of infectious diseases and the data of etiological characteristics, and uses big data analysis tools and the statistical characteristics of etiological data to comprehensively process the data. By integrating and analyzing data from different sources, thematic information related to each type of infectious disease data is mined to build the infectious disease thematic database data.

[0074] Optionally, this step may include:

[0075] Based on big data analysis tools and statistical characteristics of etiological data, the basic data of infectious diseases are integrated and analyzed to obtain the infectious disease subject database data corresponding to each type of infectious disease data.

[0076] It can be seen that this step can obtain thematic database data for each type of infectious disease data, providing more accurate data support for infectious disease monitoring.

[0077] S005, conduct infectious disease monitoring based on all infectious disease database data and medical condition monitoring data to obtain monitoring results;

[0078] Based on S004, this step integrates the infectious disease database data and the medical situation monitoring data, performs statistical analysis from multiple dimensions such as spatial distribution, personal characteristics distribution, and time distribution, and compares it with the outpatient and inpatient data of hospitals and institutions to calculate the comprehensive consistency rate. When the comprehensive consistency rate is greater than the preset value, reasoning is performed based on the infectious disease risk warning model to obtain the monitoring results.

[0079] Optionally, this step may include:

[0080] Step 1: Statistical analysis of infectious disease database data based on spatial distribution, personal characteristic distribution, and time distribution to obtain infectious disease statistics;

[0081] Step 2, obtain the outpatient and inpatient data of the hospital and analyze them to get the medical situation;

[0082] Step 3: Compare and process the infectious disease statistics and medical treatment conditions through the national network reporting card and the integrated platform to obtain the comparison results;

[0083] Step 4, calculating the comprehensive consistency rate based on the comparison results;

[0084] Step 5: When the comprehensive consistency rate is greater than the preset value, the infectious disease statistical data and medical treatment conditions are inferred based on the infectious disease risk warning model to obtain the risk monitoring results.

[0085] It can be seen that the infectious disease situation can be monitored comprehensively and accurately, and the reliability of the monitoring results can be improved.

[0086] Furthermore, when the comprehensive consistency rate is greater than a preset value, the infectious disease statistical data and medical treatment conditions are inferred based on the infectious disease risk warning model to obtain the risk monitoring results, and the process may include:

[0087] Step 1: Train the initial model based on infectious disease statistical training data and medical condition training data to obtain an infectious disease risk warning model;

[0088] Step 2: Infer the infectious disease statistics and medical treatment conditions based on the infectious disease risk warning model to obtain risk monitoring results.

[0089] S006, perform early warning processing based on the monitoring results.

[0090] Optionally, this step may include:

[0091] When the monitoring results include abnormal development data of infectious diseases, an early warning message is sent.

[0092] In summary, this embodiment can standardize data source management and ensure the accuracy and completeness of data through the registration process of the data source, provide a reliable data source for subsequent processing, and then obtain the original infectious disease data according to the data source configuration, so as to obtain the required materials efficiently and accurately. By processing data using infectious disease model rules, the original data can be converted into more valuable basic infectious disease data, laying a good foundation for subsequent integrated analysis. By combining the basic infectious disease data and the etiological characteristic data for processing, the thematic library data for each type of infectious disease data can be obtained, providing more accurate support for monitoring. The integrated infectious disease thematic library data and the medical condition monitoring data can be used for monitoring, which can comprehensively and accurately monitor the infectious disease situation and improve the reliability of the monitoring results.

[0093] The following is a more specific example to further illustrate the infectious disease risk warning data processing method provided by the present application.

[0094] Please refer to Figure 2 , Figure 2 A flowchart of another infectious disease risk warning data processing method provided in an embodiment of the present application.

[0095] In this embodiment, the method may include:

[0096] The present invention provides a method for early warning of infectious disease risk, comprising:

[0097] Step 102, registering a data source;

[0098] Step 104, providing a channel for data connection;

[0099] Step 106, processing and analyzing the data through infectious disease model rules, and providing data support for monitoring and early warning through computational learning and deep computational learning;

[0100] Step 108, based on the basic data of infectious diseases and the information related to etiology, data integration and data analysis are performed to form an infectious disease database;

[0101] Step 110, apply the previous result data to the infectious disease theme application to generate an infectious disease monitoring and early warning system, on this basis, conduct a comprehensive analysis and judgment of various thematic data, and form a normalized early warning and prediction model for the entire system through the judgment process.

[0102] In this embodiment, monitoring and early warning of infectious diseases are realized, and the discovery and prediction of various infectious diseases can be fully implemented through continuous optimization, learning, correction, and operation and maintenance of the model.

[0103] In the above embodiment, step 102 includes: determining data sources, data ranges, data connection methods and data connection frequencies, daily data volume assessment, historical data volume assessment, and data usage categories.

[0104] In the above embodiment, step 104 includes: the module is a bridge between the traditional relational database (such as MySQL, Oracle, SQL Server, etc.) and the Hi ve library, imports the data of the relational database into Hi ve, and can also extract and export the data from the Hi ve library to the relational database, and uses the MapReduce batch processing method to transmit data. In the above embodiment, the relevant information includes data source configuration, synchronization task import, synchronization task export and relational database synchronization.

[0105] In the above embodiment, step 106 includes: the module realizes the functions of task creation, process management, processing task management, solution management, data query, function management, resource management, etc. The module completes data cleaning, calculation, and scheduling, and supports rich data query capabilities, including spark, hi ve, tri no, etc. According to your business needs, you can use a suitable method to complete the reprocessing of data, so that the data is easier to use and improve the availability, reuse, ease of use, and practicality of the data.

[0106] In the above embodiment, step 108 includes: according to the infectious disease rule library, through big data analysis tools, automatically generating an infectious disease subject library according to the statistical model of each disease type, wherein each infectious disease has an independent logical data result, and the labeling of each infectious disease data is achieved through natural language analysis and other means.

[0107] In the above embodiment, step 110 includes: gathering infectious disease subject database data, generating monitoring data of medical treatment conditions and analysis modules related to infectious disease topics based on infectious disease basic data and infectious disease analysis data, generating infectious disease report verification based on comparison with State Grid's large-scale epidemic network reporting card data and integrated platform data, and generating a risk warning module based on data monitoring and early warning model control.

[0108] The present invention provides an application software infectious disease monitoring and early warning 110, including: infectious disease topics 202, for infectious disease topic analysis and statistics; medical situation monitoring 204, for medical institution medical situation monitoring, key hospitals and key departments abnormal situation analysis; infectious disease report verification 206, for national network data and integrated platform data comparison, analysis of hospital data upload quality and infectious disease underreporting and other issues; infectious disease report verification 208, for infectious disease prediction and early warning real-time monitoring and analysis of infectious disease related data, machine learning, deep learning, etc., can quickly analyze and process a large amount of data, and provide real-time prediction and early warning results. The system adopts the form of data incremental push, regularly pushes data every day, regularly completes infectious disease report analysis, and automatically learns and automatically corrects short-term fluctuation data. The infectious disease prediction and early warning system can display the prediction and early warning results in a visual way.

[0109] In the above embodiment, the infectious disease topic 202 automatically generates an infectious disease topic statistics module through analysis, and the patient's medical information, laboratory tests, medical impact examinations and patient medication information can be retrieved according to the case database.

[0110] In the above embodiment, the infectious disease special statistics module also includes an infectious disease special analysis module, which automatically generates an abnormal warning report based on the infectious disease statistical results, issues a warning prompt for growth exceeding a set threshold within a period, and generates three-dimensional distribution statistics for each disease, including spatial distribution, personal signs and time distribution.

[0111] In the above embodiment, the spatial distribution includes administrative divisions and functional divisions, and the map can dynamically switch data according to the time range, accurate to streets and permanent addresses.

[0112] In the above embodiment, the personal characteristics include the patient's name, age and occupation.

[0113] In the above embodiment, the time distribution includes consultation time, onset time, clear diagnosis time, pathogen sampling time, admission time, and discharge time, and the moving average and number of cases can be counted according to the day, week, and month dimensions.

[0114] In the above embodiment, the medical visit monitoring 204 automatically generates a medical institution medical visit analysis by analyzing the outpatient and inpatient data of the medical institution.

[0115] Among them, the medical consultation analysis module also includes abnormal medical consultation situation analysis. If the growth within the period exceeds the set threshold, an early warning prompt will be issued, and the distribution of the medical consultation population will be automatically generated. The number of medical consultations will generate an early warning line based on the moving average, intuitively displaying the changes in daily data volume.

[0116] Key departments for medical treatment monitoring: including outpatient clinics (respiratory medicine, fever clinic, infectious disease, emergency department, pediatrics, intestinal medicine, and gastroenterology), and inpatient clinics (respiratory medicine, fever clinic, infectious disease, intensive care unit, pediatrics, intestinal medicine, and gastroenterology). Inpatient monitoring also includes monitoring of the number of inpatients and the number of beds in the hospital.

[0117] In the above embodiment, the infectious disease report verification 206 generates the comparison analysis results of the State Grid reporting card and the integrated platform and the comparison results of the integrated platform and the State Grid reporting card based on the comparison of the State Grid data and the integrated platform data, and calculates the comprehensive consistency rate based on the comparison results.

[0118] Please refer to Figure 3 , Figure 3 A schematic diagram comparing the State Grid reporting card and the integrated platform provided in the embodiment of the present application.

[0119] like Figure 3 As shown, the steps for comparing the State Grid reporting card with the integrated platform include: first, being result-oriented, formulating infectious disease comparison result indicators according to needs, and then, based on the rules set by the indicators, and taking the district, medical institution, date range, infectious disease type and infectious disease name as dynamic variables, comparing the State Grid reporting card with the integrated platform data to generate a summary of the results, including no medical records on the State Grid reporting card platform, non-disease diagnosis / positive test on the State Grid reporting card platform, and diagnosis / positive test on the platform but not reported to the State Grid.

[0120] In the above embodiment, the comparison and analysis results of the State Grid reporting card and the integrated platform include multiple result indicators: the number of State Grid reporting cards, the State Grid reporting card and the platform has a positive diagnosis / test, the State Grid reporting card platform has no medical record, and the State Grid reporting card platform has no disease diagnosis / test positive.

[0121] In the above embodiment, the comparison results between the integrated platform and the State Grid report card include multiple result indicators: the platform has a diagnosis, the platform has a positive test, the platform has a diagnosis / positive test and the State Grid reports a card, and the platform has a diagnosis but the State Grid does not report a card.

[0122] In the above embodiment, infectious disease risk warning 208 establishes a data model based on the source of monitoring data and the characteristics of warning data, performs strategy iteration based on the analysis results, optimizes the algorithm in a timely manner, evaluates the model in a timely manner, and deploys the model after the model training obtains the optimal solution. Warning signals are generated according to the model rules, and the warning signals can be automatically classified and judged, and corresponding prevention and control measures are taken for different signals. The platform can send warning signals to relevant personnel in a variety of ways. These reminders can include SMS, email, mobile application push, etc., to remind relevant personnel to pay attention to epidemic warning information and take prevention and control measures in a timely manner.

[0123] The steps of infectious disease risk warning include: first, monitoring the data source, forming a database through extraction, conversion, cleaning, and loading, judging the integrity, standardization, and relevance of the data, and establishing a complete data quality assurance system for the entire process of business data acquisition, inspection, and use, providing data quality control functions covering the entire life cycle of business unit data quality analysis feedback, data extraction, data conversion, data cleaning, data loading, and data use, and then establishing a complete data quality assurance system for the entire process of business data acquisition, inspection, and use. Then, the modeling and prediction and warning process is carried out. First, feature engineering identification is carried out to convert the original data into features that can be recognized and understood by the machine learning model. Secondly, in the process of infectious disease prediction and warning, the process of constantly adjusting the prediction and warning strategy according to new data and information is also needed in the process of infectious disease prediction and warning. Then, the accuracy, timeliness, reliability, etc. of the infectious disease prediction and warning model are evaluated, and finally the model is deployed to the warning system and monitored.

[0124] In the above embodiment, feature engineering includes epidemiological features: including the transmission route, incubation period, morbidity, mortality rate, etc. of infectious diseases. Environmental factors: including climate, temperature, humidity, air quality, etc. Population factors: including population density, population age structure, population health status, etc. Social factors: including economic development level, traffic conditions, sanitary conditions, etc. The specific steps of feature engineering are as follows:

[0125] Data cleaning: Clean the raw data to remove outliers and missing values.

[0126] Feature selection: Select features related to the prediction target from the original data.

[0127] Feature transformation: Transform the selected features to make them more suitable for machine learning models.

[0128] Feature dimensionality reduction: Reduce the dimensionality of features, reduce the number of features, and improve the generalization ability of the model.

[0129] In the above embodiment, the strategy iteration includes the following steps: Collecting data and information: collecting epidemiological data, environmental data, population data and social data of infectious diseases.

[0130] Analyze data and information: Analyze the collected data and information to discover the key factors for prediction and early warning.

[0131] Build model: Build a prediction and early warning model and train the model.

[0132] Evaluate the model: Evaluate the prediction accuracy of the model and adjust the model based on the evaluation results.

[0133] Deploy the model: Deploy the model to the early warning system and monitor it.

[0134] In the above embodiment, the algorithm optimization includes the following steps:

[0135] Select algorithm: Select an appropriate prediction and early warning algorithm.

[0136] Parameter adjustment: Adjust the algorithm parameters to improve prediction accuracy.

[0137] Evaluate the model: Evaluate the prediction accuracy of the model and adjust the model based on the evaluation results.

[0138] Deploy the model: Deploy the model to the early warning system and monitor it.

[0139] In the above embodiment, the model evaluation includes the following steps:

[0140] Define evaluation metrics: Define metrics for evaluating model performance, such as accuracy, recall, F1 value, area under the ROC curve (AUC), etc.

[0141] Collect data: Collect data for evaluating the model, which is usually a separate dataset from the data used to train the model.

[0142] Evaluate the model: Use evaluation metrics to evaluate the model.

[0143] Analyze results: Analyze the evaluation results and adjust the model based on the results.

[0144] Deploy the model: Deploy the model to the early warning system and monitor it.

[0145] In the above embodiment, model deployment includes the following steps: selecting a suitable early warning system that meets the needs of the model and can meet the usage requirements of the public health department; deploying the model into the early warning system, and testing it to ensure that the model can work properly.

[0146] Monitor the model: Monitor the performance of the model and make adjustments as needed.

[0147] It can be seen that through this embodiment, multi-source monitoring of various infectious diseases can be carried out, and through the integration of multi-channel and multi-directional monitoring data, various thematic databases serving prediction and early warning can be formed. On this basis, various thematic data can be comprehensively analyzed and judged, and the judgment process can be formed into a normalized early warning prediction model for the whole system. Finally, through the continuous optimization, learning, correction, and operation and maintenance of the model, the discovery and prediction of various infectious diseases can be fully implemented.

[0148] The risk warning data processing device provided in an embodiment of the present application is introduced below. The risk warning data processing device and the risk warning data processing method described below can be referenced to each other.

[0149] Please refer to Figure 4 , Figure 4 A schematic diagram of the structure of a risk warning data processing device for infectious diseases provided in an embodiment of the present application.

[0150] In this embodiment, the device may include:

[0151] The data source registration module 10 is used to register the corresponding initial data source based on the received data source parameters to obtain the data source;

[0152] A data acquisition module 20, configured to acquire data from a data source based on a corresponding data source configuration to obtain original infectious disease data;

[0153] The data processing module 30 is used to process the original infectious disease data through the infectious disease model rules to obtain the basic infectious disease data;

[0154] The data integration processing module 40 is used to perform data integration and data analysis based on the basic data of infectious diseases and the etiological characteristic data to obtain the infectious disease subject database data corresponding to each infectious disease data type;

[0155] The infectious disease data monitoring module 50 is used to monitor infectious diseases based on all infectious disease database data and medical condition monitoring data to obtain monitoring results;

[0156] The monitoring and early warning module 60 is used to perform early warning processing based on the monitoring results.

[0157] This application also provides risk warning data processing equipment, please refer to Figure 5 , Figure 5 This is a schematic diagram of the structure of a risk warning data processing device provided in an embodiment of the present application. The risk warning data processing device may include:

[0158] Memory for storing computer programs;

[0159] A processor, when used to execute a computer program, can implement the steps of any of the above-mentioned infectious disease risk warning data processing methods.

[0160] like Figure 5 , which is a schematic diagram of the composition structure of a risk warning data processing device, the risk warning data processing device may include: a processor 10, a memory 11, a communication interface 12 and a communication bus 13. The processor 10, the memory 11, and the communication interface 12 all communicate with each other through the communication bus 13.

[0161] In the embodiment of the present application, the processor 10 may be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0162] The processor 10 may call a program stored in the memory 11. Specifically, the processor 10 may execute operations in an embodiment of the abnormal IP identification method.

[0163] The memory 11 is used to store one or more programs, which may include program codes, and the program codes include computer operation instructions. In the embodiment of the present application, the memory 11 at least stores programs for implementing the following functions:

[0164] Registering the corresponding initial data source based on the received data source parameters to obtain the data source;

[0165] Acquire data from the data source based on the corresponding data source configuration to obtain original infectious disease data;

[0166] Processing the original infectious disease data using infectious disease model rules to obtain basic infectious disease data;

[0167] Perform data integration and data analysis based on the basic infectious disease data and etiological characteristic data to obtain infectious disease subject database data corresponding to each infectious disease data type;

[0168] Conduct infectious disease monitoring based on all the infectious disease database data and medical condition monitoring data to obtain monitoring results;

[0169] Perform early warning processing based on the monitoring results.

[0170] In a possible implementation, the memory 11 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function, etc.; the data storage area may store data created during use.

[0171] In addition, the memory 11 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0172] The communication interface 12 may be an interface of a communication module, and is used to connect to other devices or systems.

[0173] Of course, it should be noted that Figure 5 The structure shown does not constitute a limitation on the risk warning data processing device in the embodiment of the present application. In actual applications, the risk warning data processing device may include Figure 5 More or fewer components than shown, or combinations of certain components.

[0174] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned infectious disease risk warning data processing methods can be implemented.

[0175] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0176] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0177] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0178] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0179] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0180] The above is a detailed introduction to a method for processing risk warning data of infectious diseases, a device for processing risk warning data of infectious diseases, a risk warning data processing equipment, and a computer-readable storage medium provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for processing risk warning data of infectious diseases, characterized in that: include: Registering the corresponding initial data source based on the received data source parameters to obtain the data source; Acquire data from the data source based on the corresponding data source configuration to obtain original infectious disease data; Processing the original infectious disease data using infectious disease model rules to obtain basic infectious disease data; Perform data integration and data analysis based on the basic infectious disease data and etiological characteristic data to obtain infectious disease subject database data corresponding to each infectious disease data type; Conduct infectious disease monitoring based on all the infectious disease database data and medical condition monitoring data to obtain monitoring results; Perform early warning processing based on the monitoring results.

2. The risk warning data processing method according to claim 1 is characterized in that: The corresponding initial data source is registered based on the received data source parameters to obtain the data source, including: Based on the received data source parameters, determine the data source, data scope, data connection method, data connection frequency, daily data volume assessment, historical data volume assessment, and data usage, and use them as registration parameters; The initial data source is registered based on the registration parameters to obtain a data source.

3. The risk warning data processing method according to claim 2 is characterized in that: Based on the corresponding data source configuration, data is acquired from the data source to obtain original infectious disease data, including: Building a connection relationship between the data source and the local database based on the data source configuration; Data is acquired based on the connection relationship to obtain the original infectious disease data.

4. The risk warning data processing method according to claim 3 is characterized in that: Based on the basic data of infectious diseases and the data of etiological characteristics, data integration and data analysis are performed to obtain infectious disease subject database data corresponding to each type of infectious disease data, including: Based on big data analysis tools and statistical characteristics of etiological data, the basic data of infectious diseases are integrated and analyzed to obtain infectious disease subject database data corresponding to each type of infectious disease data.

5. The risk warning data processing method according to claim 4 is characterized in that: Based on all the infectious disease database data and medical condition monitoring data, infectious disease monitoring is carried out to obtain monitoring results, including: Performing statistical analysis on the infectious disease database data based on spatial distribution, personal characteristic distribution, and time distribution to obtain infectious disease statistical data; By obtaining the data of outpatient and inpatient visits of hospitals and institutions and analyzing them, we can get the medical situation; By using the national network reporting card and the integrated platform, the statistical data of the infectious diseases and the medical treatment conditions are compared and processed to obtain the comparison results; Calculating a comprehensive consistency rate based on the comparison results; When the comprehensive consistency rate is greater than a preset value, the infectious disease statistical data and the medical treatment situation are inferred based on the infectious disease risk warning model to obtain a risk monitoring result.

6. The risk warning data processing method according to claim 5 is characterized in that: The infectious disease statistical data and the medical treatment situation are inferred based on the infectious disease risk early warning model to obtain risk monitoring results, including: The initial model is trained based on the infectious disease statistical training data and the medical condition training data to obtain the infectious disease risk warning model; The infectious disease statistical data and the medical treatment situation are inferred based on the infectious disease risk early warning model to obtain the risk monitoring result.

7. The risk warning data processing method according to claim 6 is characterized in that: Perform early warning processing based on the monitoring results, including: When the monitoring results include data on abnormal development of infectious diseases, an early warning message is sent.

8. A risk warning data processing device for infectious diseases, characterized in that: include: A data source registration module is used to register the corresponding initial data source based on the received data source parameters to obtain the data source; A data acquisition module, used to acquire data from the data source based on the corresponding data source configuration to obtain original infectious disease data; A data processing module is used to process the original infectious disease data using infectious disease model rules to obtain basic infectious disease data; A data integration processing module is used to perform data integration and data analysis based on the basic data of infectious diseases and the etiological characteristic data to obtain infectious disease subject database data corresponding to each type of infectious disease data; An infectious disease data monitoring module is used to monitor infectious diseases based on all the infectious disease database data and medical condition monitoring data to obtain monitoring results; The monitoring and early warning module is used to perform early warning processing based on the monitoring results.

9. A risk warning data processing device, characterized in that: include: Memory for storing computer programs; A processor, used to implement the steps of the risk warning data processing method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the risk warning data processing method according to any one of claims 1 to 7 are implemented.

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