Intelligent investigation and collection middleware device for food-borne diseases

Abstract

The invention discloses a middleware device for intelligent investigation and acquisition of food-borne diseases, relates to the field of intelligent investigation and acquisition of food-borne diseases, and provides a middleware device comprising a data acquisition component, a food-borne disease information filling component and an information reporting component. The middleware device is used for collecting diagnosis information in a hospital information management system to automatically trigger and call a food-borne disease information filling assembly, and basic information of a patient is automatically brought in. Clinical doctors perfect food-borne disease information, food classification and processing or packaging mode information are automatically associated and filled after food names are filled and exposed, and the doctors in the public health department access the auditing terminal in the public health department to modify and auditing food-borne cases and report the food-borne cases to the national food-borne disease case monitoring system. The problems that an existing food-borne disease monitoring method is long in time consumption, poor in timeliness and large in extra workload are solved, and the efficiency of intelligent investigation and collection of food-borne diseases is improved.

Description

Technical Field

[0001] This application relates to the field of intelligent investigation and collection of foodborne diseases, and particularly to an intermediate device for intelligent investigation and collection of foodborne diseases. Background Art

[0002] With the development of information technology, the informatization of primary healthcare in China has achieved further results, and the software / hardware level has been greatly improved, which plays a positive role in reducing the burden and increasing the efficiency of primary healthcare institutions in the data collection and reporting process. However, due to the uneven medical levels in different regions of China, the informatization construction of medical institutions in relatively economically backward regions is relatively backward. Currently, more than 65,000 medical institutions across the country are carrying out monitoring work as foodborne disease case monitoring points in the collection and reporting of foodborne disease cases. About 2 million case data are collected annually, and only more than 400 medical institutions report data using the system docking data transmission method. Most medical institutions still report data using methods such as original manual filling of case cards, paper filling, and secondary filling in the system. The input efficiency is low, and foodborne cases cannot be reported in a timely manner for data summary and analysis to track the incidence and epidemic trends of important foodborne diseases. At the same time, it also causes heavy workload and great pressure on front-line personnel in primary medical institutions, affecting the efficiency of medical work. Problems such as low data quality, missed reports, and late reports have long existed. Currently, due to problems such as the lack of informatization professionals, insufficient capital investment, and weak infrastructure in medical institutions, it is difficult to carry out intelligent data collection and reporting. There are significant differences in the informatization levels of medical institutions at all levels across the country. There are many manufacturers of hospital information management systems (which can be called HIS systems), and the standards are not unified. Information such as the classification and diagnosis of foodborne diseases varies, and the cost of self-modifying hospitals to achieve automatic collection of foodborne disease cases is high. The existing interface reporting method is only applicable to medical institutions with a relatively high level of informatization construction due to the large amount of modification. Most medical institutions cannot carry out intelligent data collection and reporting due to insufficient funds. Exploring new data collection and reporting methods is of great significance for strengthening the understanding of the incidence trends and outbreak situations of important foodborne diseases by medical institutions.

[0003] The informatization levels of data collection and reporting personnel in primary medical institutions vary. Currently, there are some data filling specialists in primary medical institutions with low computer operation skills and older ages. Due to their unfamiliarity with computer and application system operations, lack of training, and unfamiliarity with the collection and reporting process of foodborne cases, it is easy to cause situations such as missed reports and late reports. It is urgent to reduce the manpower burden on relevant departments in primary medical institutions for foodborne case report data collection activities, improve the efficiency and quality of foodborne case reporting work, which requires new data collection technologies to be easy to operate and capable of realizing intelligent collection.

[0004] At the same time, the amount of data filled in the foodborne disease system is large, but the informatization level of most hospitals is not high, and the workload of the filling staff is heavy. In view of the current status of data collection in medical institutions, it is urgent to explore a new data collection and reporting method to achieve intelligent automatic data collection. At the same time, the new collection method should adapt to the informatization level of medical institutions, reduce the system transformation cost of medical institutions, and reduce the workload of medical workers in filling in forms. Summary of the Invention

[0005] The purpose of this application is to provide a middleware device for intelligent investigation and collection of foodborne diseases, which solves the problems of long time consumption, poor timeliness, and large additional workload in the existing foodborne disease monitoring methods, and improves the efficiency of intelligent investigation and collection of foodborne diseases.

[0006] To achieve the above purpose, this application provides the following solutions:

[0007] This application provides a middleware device for intelligent investigation and collection of foodborne diseases, which includes: a data collection component, a foodborne disease information filling component, and an information reporting component;

[0008] The data collection component is used to obtain the diagnosis conclusions filled in by clinicians in the information management systems of each hospital, and match the fields in the diagnosis conclusions made by clinicians with the diagnosis conclusions in the foodborne disease diagnosis database; the foodborne disease diagnosis database is used to store foodborne disease diagnosis conclusion data;

[0009] The foodborne disease information filling component is used to automatically pop up an information filling page when the fields in the diagnosis conclusions made by doctors in the hospital information management system match the diagnosis conclusions in the foodborne disease diagnosis database, and automatically fill in the basic information of foodborne disease patients in each hospital information management system into the information filling page;

[0010] The information reporting component is used to report the information filled in the information filling page to the national foodborne disease case monitoring system.

[0011] Optionally, the data collection component is used to obtain the diagnosis conclusions filled in by clinicians in the information management systems of each hospital, and use natural language processing and semantic analysis technologies to match the fields in the diagnosis conclusions with the data in the foodborne disease diagnosis database.

[0012] Optionally, the data collection component is used to obtain the diagnosis conclusions filled in by clinicians in the information management systems of each hospital by using multi-thread technology, and match the fields in the diagnosis conclusions with the data in the foodborne disease diagnosis database.

[0013] Optionally, the foodborne disease information filling component is also used for clinicians to manually fill in the case examination information of foodborne disease patients in the automatically popped-up information filling page.

[0014] Optionally, the foodborne disease information filling component is also used to automatically associate and fill in the food classification, processing or packaging method after filling in the name of the exposed food in the information filling page according to the case examination information of the foodborne disease patients filled in by the clinician; the exposed food refers to the food that is likely to cause foodborne diseases.

[0015] Optionally, the foodborne disease information filling component is also used to automatically perform logical verification on the information entered in the information filling page.

[0016] Optionally, the information reporting component is also used to save the information entered in the information filling page in an encrypted manner to the foodborne disease case database.

[0017] Optionally, the foodborne disease intelligent investigation middleware device further includes an aggregated case analysis component for performing aggregated case analysis based on the case data of each patient stored in the foodborne disease case database.

[0018] Optionally, the information entered in the information filling page is filled in using the UTF-8 character set.

[0019] Optionally, the foodborne disease diagnosis database, the foodborne disease case database, the data collection component, the foodborne disease information filling component, and the information reporting component are deployed on the hospital front-end machine using Docker container deployment technology.

[0020] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:

[0021] The present application provides a foodborne disease intelligent investigation middleware device, which sets up a middleware device including a data collection component, a foodborne disease information filling component, and an information reporting component. The middleware device is used to collect the diagnosis information in the hospital information management system and automatically trigger the call of the foodborne disease information filling component, and the basic patient information is automatically brought in. The clinician improves the foodborne disease information, and after filling in the name of the exposed food, the food classification and processing or packaging method information are automatically associated and filled in. The public health doctor accesses the public health department review terminal to modify, review and report the foodborne disease cases to the national foodborne disease case monitoring system, which solves the problems of long time consumption, poor timeliness and large additional workload of the existing foodborne disease monitoring methods, and improves the efficiency of foodborne disease intelligent investigation and collection. Description of the Drawings

[0022] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0023] Figure 1 Schematic diagram of the technical route of an intelligent investigation and collection middleware device for foodborne diseases provided in an embodiment of the present application;

[0024] Figure 2 Schematic diagram of the basic information automatic filling page in the information filling page provided in an embodiment of the present application;

[0025] Figure 3 Schematic diagram of the page where a clinician manually fills in the case examination information of a foodborne disease patient in the information filling page provided in an embodiment of the present application;

[0026] Figure 4 Schematic diagram of the exposure information page provided in an embodiment of the present application;

[0027] Figure 5 Schematic diagram of the principle of automatic intelligent classification of exposed foods provided in an embodiment of the present application;

[0028] Figure 6 Schematic diagram of the specimen information page provided in an embodiment of the present application;

[0029] Figure 7 Schematic diagram of the first page of the rule setting and viewing unit provided in an embodiment of the present application;

[0030] Figure 8 Schematic diagram of the second page of the rule setting and viewing unit provided in an embodiment of the present application;

[0031] Figure 9 Schematic diagram of the first page of the review of suspected clustered cases provided in an embodiment of the present application;

[0032] Figure 10 Schematic diagram of the second page of the review of suspected clustered cases provided in an embodiment of the present application

[0033] Figure 11 Schematic diagram of the third page of the review of suspected clustered cases provided in an embodiment of the present application

[0034] Figure 12 Schematic diagram of the first page of the management of suspected clustered cases provided in an embodiment of the present application;

[0035] Figure 13This is a schematic diagram of the second page for the management of suspected clustered cases provided by an embodiment of the present application. Detailed implementation manners

[0036] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0037] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0038] Embodiment

[0039] As Figure 1 shown, this embodiment provides an intermediate device for intelligent investigation and collection of foodborne diseases. The intermediate device for intelligent investigation and collection of foodborne diseases is deployed on the hospital front-end server. After deployment, an embedded call address and a review end address for the public health department will be generated. The hospital develops and calls the embedded address according to the interface document. After the clinician makes a diagnosis conclusion related to foodborne diseases, an embedded page (the information filling page of the information reporting component) will automatically pop up.

[0040] The intermediate device for intelligent investigation and collection of foodborne diseases includes: a data collection component, an information filling component for foodborne diseases, and an information reporting component.

[0041] The intermediate device for intelligent investigation and collection of foodborne diseases further includes a foodborne disease diagnosis database, a foodborne disease case database, and an exposed food information database. Among them, the foodborne disease diagnosis database, the foodborne disease case database, and the exposed food information database constitute an overall foodborne disease database. The foodborne disease database, the data collection component, the information filling component for foodborne diseases, and the information reporting component are deployed on the hospital front-end machine using Docker container deployment technology. The foodborne disease diagnosis database is used to store foodborne disease diagnosis conclusion data, such as dietetic diarrhea, bean poisoning, etc.

[0042] Put the application (data collection component), foodborne disease database, foodborne disease information filling component, and information reporting component into a Docker container. Docker uses container technology to package them into an independent container. Upload the deployment installation packages docker.zip and update.zip of the foodborne disease intelligent investigation and collection middleware device in the Docker container to the hospital front-end machine, and complete the deployment by decompressing docker.zip and switching to docker and executing the deployment script. Docker can be started and stopped quickly. After the deployment is completed through the deployment technology, two URL addresses, namely the foodborne disease information filling component (filled in by clinicians) and the information reporting component (reviewed and reported by the public health department), are generated on the embedded microservice component page. The foodborne disease information filling component (filled in by clinicians) and the information reporting component (reviewed and reported by the public health department) can be directly accessed through the URL addresses, and the foodborne disease information filling component and the information reporting component can be directly called when the hospital information management system (which can be called the HIS system) is running. Both the foodborne disease information filling component and the information reporting component are embedded microservice components. The hospital information management system can call the two component pages.

[0043] The foodborne disease intelligent investigation and collection middleware device of this application is suitable for deployment in Linux operating system environments with different environments such as Galaxy Kylin V7 and above, Zhongbiao Kylin V7 and above, and Huawei Euler.

[0044] Real-time intelligent monitoring of report cards, using technologies such as natural language processing and semantic analysis to extract key fields based on the patient's diagnosis conclusion, and using a supervised learning diagnosis conclusion model integrated with ICD-10 coding to monitor the above key fields to achieve an automatic pop-up intelligent report card (information filling page).

[0045] Using the supervised learning diagnosis conclusion model, match the extracted key fields with the diagnosis conclusion data in the foodborne disease diagnosis database. Specifically, input the extracted key fields into the supervised learning diagnosis conclusion model, and the model calls the foodborne disease diagnosis database to achieve data matching.

[0046] The data acquisition component is used to obtain the diagnosis conclusions made by clinicians in each hospital information management system, and match the fields in the diagnosis conclusions made by clinicians in the hospital information management system with the diagnosis conclusion data in the foodborne disease diagnosis database. Specifically, the data acquisition component is used to obtain the diagnosis conclusions made by clinicians in each hospital information management system, and use natural language processing and semantic analysis technologies to match the fields in the diagnosis conclusions with the data in the foodborne disease diagnosis database. By using natural language processing and semantic analysis, the key fields are matched with the foodborne disease diagnosis database to automatically prompt filling after successful matching. After filling in the exposed food name, automatic associated filling of the exposed food name, food classification, processing or packaging method information can be realized, and automatic logical verification of the input information can be performed. Data exchange and interaction can be carried out while collecting data.

[0047] This application can use technologies such as natural language processing and semantic analysis to extract the key fields of the diagnosis conclusions of the hospital information management system according to the diagnosis conclusions in the hospital information management system, and match them with the foodborne disease diagnosis database. If the match is successful, an embedded microservice component page (the information filling page of the foodborne disease information filling component) will pop up automatically for filling. At the same time, under the security of encrypted network transmission, national cipher SM4 encryption algorithm or Base64 encryption transmission security through the component, the value-passing fields such as the patient's basic information in the hospital information management system will be transmitted to the corresponding fields of the patient's basic information in the embedded microservice component page (information filling page) in UTF-8 character set for automatic filling and display. That is, when the data acquisition component obtains the diagnosis conclusions of the hospital information management system and the basic information of the patient, it is obtained in an encrypted data manner.

[0048] The data acquisition component is used to obtain the diagnosis conclusions filled in by clinicians in each hospital information management system by using multi-thread technology, and match the fields in the diagnosis conclusions with the data in the foodborne disease diagnosis database.

[0049] Multi-threading technology: Multiple threads run simultaneously in a program. Each thread is an independent execution flow and can perform different tasks concurrently. In the collection of foodborne disease diagnosis results, multi-threading technology can divide the collection tasks into multiple subtasks, with each subtask being responsible for a thread. Currently, various types and levels of medical institutions still mostly use traditional Excel spreadsheets to collect data, calculate data, distribute data, summarize data, etc. And when the management units and utilization units of medical institution data want to utilize and analyze the data collected in these traditional ways, the drawbacks are obvious. With the continuous accumulation of historical data, a large amount of incoming data needs to be collected, docked, stored, etc. for each data holding unit one by one, which is extremely time-consuming and requires professional and data-familiar personnel to verify the data one by one, posing a great challenge to the manpower demand and reserve. The middleware device provided by the present invention can use multi-threading technology to improve the efficiency and stability of data collection and acquisition, and realize the import and intelligent filling of foodborne disease diagnosis results.

[0050] The foodborne disease information filling component is used to automatically pop up an information filling page when the fields in the diagnosis conclusion under the clinical doctor in the hospital information management system match the data in the foodborne disease diagnosis database, and automatically fill the basic information of foodborne disease patients (including information such as name, gender, age, address, ID number, contact phone number, etc.) in each hospital information management system into the information filling page, as Figure 2 shown.

[0051] Using the data interface (data collection component) between the hospital information management system and the middleware device, each field of the diagnosis information and the patient's basic information is transmitted using the character set UTF-8; the data field formats of each unit's hospital information management system and the middleware device are interactively unified. That is, the information such as the basic information of foodborne disease patients in the information filling page is filled in the way of the character set UTF-8.

[0052] The foodborne disease information filling component is also used for the clinical doctor to manually fill in the case examination information of foodborne disease patients in the automatically popped-up information filling page, as Figure 3 shown, including inquiring about the patient's symptoms and signs, as well as dietary information, relevant information such as the place of food purchase, purchase time, eating time, etc.

[0053] The foodborne disease information filling component also realizes the automatic associated filling of information such as the name of the exposed food, food classification, processing or packaging method, etc., and automatically performs logical verification on the input information.

[0054] The foodborne disease information filling component is also used to automatically fill in the name, classification, and packaging type of the exposed food in the information filling page according to the case examination information of the foodborne disease patient filled in by the clinician; the exposed food refers to the food that is likely to cause foodborne diseases, such as Figure 4 shown. Among them, the automatic filling of the exposed food name, classification, and packaging type is automatically matched and filled according to the exposed food name, classification, and packaging type in the exposed food information database. For example, if the case examination information filled in by the clinician includes watermelon, it is automatically classified as fruits and their products (including preserved fruits and candied fruits). The exposed food information database is initially the intelligent classification data summarized from the data reported over the years. After that, the hospital continuously reports and continuously summarizes and supplements relevant content based on the corresponding model methods on this basis. The data volume continues to expand and the content becomes more and more comprehensive. Methods such as K-means and hierarchical clustering are used to automatically associate the exposed food name with the food classification and packaging type. As Figure 5 shown, specifically, the automatic intelligent classification of exposed foods is based on the dataset of foodborne disease case reports reported nationwide since 2012. Two algorithms, K-means clustering analysis and hierarchical clustering, are used to automatically mine the potential patterns and rules between the exposed food name and the food classification. In order to ensure the accuracy and reliability of the machine learning model, the clustered dataset is scientifically divided into a training set and a test set. The training set is used for the training and learning of the model, while the test set is used to verify the performance of the model and ensure its performance on unknown data. After the model training is completed, according to the clustering results and the actual situation of the food classification, a rule library of the exposed food name and the food classification is extracted and formed. The rule library (i.e., the exposed food information database) is the basis for the component classification decision, containing 1952 verified and optimized classification rules, which can accurately classify the exposed food into the corresponding food classification. Through machine learning, the exposed food name is automatically classified into the correct food classification, and finally the automatic association of the exposed food name with the food classification and packaging type is realized.

[0055] The exposed food information database is also a unit of the foodborne disease data database.

[0056] The foodborne disease information filling component is also used to automatically perform logical verification on the information entered in the information filling page.

[0057] The immediate verification of the reported information refers to the verification of each field filled in on the page. For example, if a certain field is a required item, the visit time must be greater than the onset time, and when the symptom information is [diarrhea], the character and frequency cannot be empty, etc.

[0058] The information reporting component is used to review and report the information filled in by clinicians on the information filling page to the National Foodborne Disease Case Surveillance System. The department responsible for foodborne disease review in the hospital (public health doctors) conducts information review in the information reporting component (reporting at the public health department review end), and after review, clicks "Review and Report" to directly synchronize the data to the provincial server. If there is a provincial foodborne disease case surveillance system in the province, the data can also be synchronized to the provincial foodborne disease case surveillance system, and further reported to the National Foodborne Disease Case Surveillance System (corresponding to Figure 1 the national case system in

[0059] The information reporting component is also used to save the information entered on the information filling page to the foodborne disease case database.

[0060] The foodborne disease case database stores basic information of foodborne patients, case exposure information, monthly summary information, specimen information (such as Figure 6 ), specimen test item information, strain information, etc. Among them, the monthly summary information, specimen information, specimen test item information, and strain information are filled in by public health doctors on the data reporting page. As Figure 6 shown is a partial information filling page in the information reporting component.

[0061] Since the foodborne disease case database will store a large amount of foodborne disease information of patients, the foodborne disease intelligent investigation middleware device further includes an aggregated case analysis component, which is used to perform aggregated case analysis based on the case data of each patient in the foodborne disease case database. The aggregated case analysis component includes a rule viewing unit, a suspected aggregated case review unit, and a suspected aggregated case management unit.

[0062] Using the rule setting interface of the rule setting and viewing unit, by filling in detailed rule information and selecting to save, the aggregation requirements for aggregated cases can be set. As Figure 7 shown. This component can synchronize the aggregation rules of the National Foodborne Disease Case System. The aggregation rules of aggregated cases can be viewed at the public health department review end. As Figure 8 shown. Figure 7 The foodborne disease intelligent exchange system in

[0063] is the foodborne disease intelligent investigation middleware device of this application. Figure 9 、 Figure 10 and Figure 11 shown.

[0064] Enter the suspected cluster case management interface using the suspected cluster case management unit. The search area is located above this interface. You can search according to conditions, and the relevant content will be displayed below the interface. Select the data to be processed and click the [View Details] interface to display the detailed content of this data. For example Figure 12 and Figure 13 as shown

[0065] The foodborne disease case database is based on the foodborne disease information and data of medical institutions and provides data support for each application party. After processing the obtained data and materials through data cleaning technology, multi-source data fusion technology, and data security encryption technology, they are stored in the foodborne disease case database as needed. As a basic information database and benchmark database, the foodborne disease case database enables one-time collection and multiple uses, supporting dynamic query, call, and extraction of data. Through system collaborative docking, data sharing and reporting, and form auxiliary generation, the data of the hospital information management system and foodborne disease data are interconnected and integrated

[0066] The data transmission between the hospital information management system and each component is encrypted using the national secret SM4 encryption algorithm and Base64 encryption method. On the one hand, when the data acquisition component obtains the diagnosis conclusion of the hospital information management system, it is obtained through data encryption. On the other hand, when storing the information of each patient entered on the information filling page into the foodborne disease case database, the data needs to be stored in an encrypted manner. When the public health department's review terminal reviews and reports data to the national foodborne disease case monitoring system, encrypted transmission is also used

[0067] The national foodborne disease case monitoring system may involve situations such as business field adjustments or dictionary adjustments. The middleware device needs to be synchronized with the business of the national foodborne disease case monitoring system (to achieve synchronization between the hospital client and the national case monitoring system), construct the monitoring background management of the middleware device, and implement the function of batch release and automatic update, so as to achieve the unified update of Shell scripts and each component. When there is a new version of the application program due to changes in business fields or dictionaries, the Shell script under the hospital front-end Docker automatically downloads the new version of the software, backs up the old version, installs the new version, automatically configures files, and restarts services through timed automatic execution commands, automatically updating to the new version of the application program, reducing the need for manual operations, improving the efficiency of the update process, and reducing the risk of human errors

[0068] The middleware device of the present application provides a page for filling in information on foodborne diseases in medical institutions. When clinicians fill in diagnostic information through the hospital information management system, the middleware technology for data collection and multi-threading technology are used to match and automatically compare the key fields filled in by multiple clinicians in different hospital information management systems with the foodborne disease database, identify foodborne disease information through natural language processing and semantic analysis technology, and automatically fill in the required fields according to the fields to be filled, filling the key fields into the page for filling in foodborne disease information in medical institutions, and supporting prompting for filling in the hospital information management system. The middleware device of the present application can automatically collect the corresponding key field information of foodborne diseases through the embedded microservice component and data extraction technology when clinicians make a diagnosis conclusion in the hospital information management system, and prompt in the hospital information management system that this case may be a foodborne disease case and foodborne disease information needs to be filled in, and support saving the filled foodborne disease information to the foodborne disease case database.

[0069] The middleware device is embedded in the original system structure (medical institution system) through the middleware technology for data collection to form a Sever / Client architecture. The first layer is the client, the second layer is the middleware, and the third layer is the server. The middleware is an independent system software or service program. The middleware adopted will be above the operating systems of the client / server, manage computing resources and network communication. The background is an independently running Server, and the foreground is the API. Different types of ClientAPIs are provided according to the different applicable platforms and development environments of the middleware. Through the ClientAPI of the middleware, the application system is connected to the Server of the middleware. All service calls first access the Server of the middleware and are forwarded through the middleware. Finally, through the middleware technology, the diagnosis conclusion is automatically filled in and filled in intelligently, and data collection / bringing-in is carried out through the API interface method.

[0070] Relying on big data, artificial intelligence technology, deep learning based on Python, etc., deep learning is carried out through multiple extraction simulations and extraction operations, and finally supports extracting reasonable data and specified required data on demand. It supports data export and call in formats such as XML and CSV, and supports custom multi-field and multi-condition data extraction.

[0071] The middleware device of this application follows the idea and overall architecture of "unified data collection and business application sharing", and is planned and designed according to the architecture of the application support platform; application development is based on the JavaEE technology system, adopts microservices and distributed architecture, and according to the model-view-controller - MVC system framework mode, realizes the multi-layer architecture of the system to reduce the coupling between systems and improve reusability; adopts a layered technology architecture, uses jsp+javascript for the front end, Spring Boot technology for the basic service architecture, Nginx technology for the gateway application, and Redis caching technology; for the processing and application of large and complex business data, big data technologies such as MySQL are adopted.

[0072] In this application, the clinician gives a diagnosis conclusion to the patient, uses natural language processing and semantic analysis to match the keyword fields of the diagnosis conclusion with the foodborne disease database, automatically triggers the call of the foodborne disease information filling component, automatically brings in the patient's basic information, and automatically associates information such as the exposed food name and food classification (such as watermelon is automatically classified as fruits and their products (including preserved fruits and candied fruits)), and the clinician perfects the foodborne disease information. The public health doctor accesses the public health department's review terminal to modify, review, report, etc. on foodborne cases, and completes the case reporting.

[0073] In this application, through the triggering of prompts by the diagnosis conclusion, the automatic bringing in of the patient's basic information, the automatic association and filling of information such as the exposed food name and food classification, and the automatic logical verification of the entered information, the burden of manual paper filling and repeated filling by front-line personnel in primary medical institutions is reduced, thus solving the problems of low data quality, accuracy and timeliness of foodborne disease case data, and realizing the data collection and exchange of multiple hospital information management systems. While the foodborne disease monitoring work can be realized, the relevant information of foodborne disease cases can be directly obtained from the hospital information management systems of each hospital without filling in the basic information of patients already existing in the hospital information management system, simplifying the data collection process, improving the networked, intelligent and digital level of foodborne disease case monitoring work, reducing the grass-roots filling burden, improving the reporting efficiency of case monitoring information, realizing the paperless of foodborne disease monitoring work, while reducing the workload of medical institutions and health departments, accelerating the speed of determining the source of foodborne diseases, timely discovering clustered cases, improving the early identification, warning and prevention and control capabilities of food safety hazards, and conveniently managing the detection information of cases and specimens.

[0074] The advantages of the middleware device of this application are as follows:

[0075] (1) Whether it is data extraction or invocation, it is implemented in the form of a foodborne disease database. Real-time data can be collected, multi-source data can be fused, and structured data can be output, improving data quality and utilization value, thereby further enhancing the reporting efficiency and reducing the occurrence of reporting errors.

[0076] (2) Intelligent reporting provides technical support for the middleware device based on the diagnosis conclusions in the hospital information management system and using technologies such as natural language processing and semantic analysis. The main features include that the extracted key fields match the foodborne disease database, automatically prompting for reporting, being able to automatically associate and fill in information such as the name of the exposed food, food classification, processing or packaging method, and automatically performing logical verification on the entered information. It is more user-friendly than the traditional reporting method. While collecting data, data exchange and interaction can be carried out, thereby further enhancing the reporting efficiency and reducing the occurrence of reporting errors.

[0077] (3) Data standardization

[0078] Using the data interface between the hospital information management system and the middleware device, the passing fields of the diagnosis conclusion and the patient's basic information are transmitted and interacted using the character set UTF-8. Promote and assist in the unification of the field formats of the hospital information management system data of each unit, greatly improving the data collection and exchange efficiency, and avoiding the sharp increase in the additional workload caused by the system data docking due to inconsistent formats.

[0079] (4) Data security encryption

[0080] Each component of the hospital information management system and the middleware device realizes transmission encryption. For example, the national secret SM4 encryption algorithm and the Base64 encryption method are applied.

[0081] (5) Strong application scalability

[0082] The provincial server opens API interfaces, and the public health department audit end of the middleware device realizes docking with the provincial server interfaces.

[0083] (6) Improving update efficiency

[0084] The monitoring background management of the middleware device realizes the function of batch release and automatic update. The Shell script automatically executes commands at regular intervals to download the new version of the software, back up the old version, install the new version, configure and restart the service, etc., reducing manual operations, improving the timeliness of updates and the efficiency of the update process, and reducing the risk of human errors.

[0085] The middleware device realizes synchronous update with the national foodborne disease case monitoring system. Adjustments to the fields and dictionaries of the national foodborne disease case monitoring system enable unified updates of the script, data intelligent collection, and exchange components.

[0086] (7) Improve the level of intelligence, which is mainly reflected in the following aspects:

[0087] The data collection middleware will be more efficient and stable, and can handle large-scale and high-concurrency access and other situations. The data collection middleware will use Docker container deployment technology to improve the efficiency and stability of the entire system.

[0088] The data collection middleware will be more secure and reliable, and can protect the security and privacy of data.

[0089] While improving the monitoring level, enhancing the network prediction ability and the level of intelligence, a large amount of labor verification costs, maintenance costs and fault analysis costs are saved.

[0090] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0091] In this article, specific examples are used to elaborate on the principles and implementation methods of this application. The descriptions of the above embodiments are only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, based on the idea of this application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A foodborne disease intelligent investigation and collection middleware device, characterized in that: The foodborne disease intelligent investigation and collection middleware device comprises: a data collection component, a foodborne disease information filling component and an information reporting component; The data acquisition component is used to obtain the diagnosis conclusions of clinicians in each hospital information management system, and match the fields in the diagnosis conclusions of clinicians with the diagnosis conclusions in the foodborne disease diagnosis database; the foodborne disease diagnosis database is used to store foodborne disease diagnosis conclusion data; The foodborne disease information filling component is used to automatically pop up an information filling page when the fields in the diagnosis conclusion of the clinician under the hospital information management system match the diagnosis conclusion in the foodborne disease diagnosis database, and automatically fill the basic information of the foodborne disease patients in each hospital information management system into the information filling page; The information reporting component is used to report the information filled in the information filling page to the national foodborne disease case monitoring system.

2. The foodborne disease intelligent investigation and collection middleware device according to claim 1 is characterized in that: The data acquisition component is used to obtain the diagnosis conclusions of clinicians in each hospital information management system, and use natural language processing and semantic analysis technology to match the fields in the diagnosis conclusions with the data in the foodborne disease diagnosis database.

3. The foodborne disease intelligent investigation and collection middleware device according to claim 1 is characterized in that: The data acquisition component is used to obtain the diagnosis conclusions filled in by clinicians in each hospital information management system using multi-threading technology, and match the fields in the diagnosis conclusions with the data in the foodborne disease diagnosis database.

4. The foodborne disease intelligent investigation and collection middleware device according to claim 1, characterized in that: The foodborne disease information filling component is also used for clinicians to manually fill in the case examination information of foodborne disease patients in the automatically popped-up information filling page.

5. The foodborne disease intelligent investigation and collection middleware device according to claim 4 is characterized in that: The foodborne disease information filling component is also used to automatically associate and fill in the food classification, processing or packaging method after filling in the name of the exposed food in the information filling page based on the case examination information of the foodborne disease patient filled in by the clinician; the exposed food refers to the food that is likely to cause foodborne diseases.

6. The foodborne disease intelligent investigation and collection middleware device according to claim 5 is characterized in that: The foodborne disease information filling component is also used to perform automatic logic verification on the information entered in the information filling page.

7. The foodborne disease intelligent investigation and collection middleware device according to claim 5, characterized in that: The information reporting component is also used to save the information entered in the information filling page in a foodborne disease case database in an encrypted manner.

8. The foodborne disease intelligent investigation and collection middleware device according to claim 7, characterized in that: The foodborne disease intelligent investigation and collection middleware device also includes a cluster case analysis component, which is used to perform cluster case analysis based on the case data of each patient stored in the foodborne disease case database.

9. The foodborne disease intelligent investigation and collection middleware device according to claim 1, characterized in that: The information entered in the information filling page is filled in using the character set UTF-8.

10. The foodborne disease intelligent investigation and collection middleware device according to claim 1, characterized in that: The foodborne disease diagnosis database, the foodborne disease case database, the data collection component, the foodborne disease information filling component and the information reporting component are deployed on a hospital front-end computer using Docker container deployment technology.

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