Cardiovascular disease information entity labeling and identification method, device and system

Abstract

The present invention discloses a method, system and device for labeling and identifying cardiovascular disease information entities, extracting full-text information from digital files or collecting data prepared for word segmentation, inputting data into a label function, performing word segmentation training on the information based on regular matching and generating labels, integrating label data and original data according to the input parameters of the model, and inputting them into the model for entity recognition model training to produce a result set and corresponding scoring results. The present invention solves the timeliness and cost issues of information entity labeling of massive digital files through model training, and allows non-algorithm personnel to quickly implement operations through program implementation. It is a great innovation in the tool category, and can be widely used in data labeling in the field of digital applications, providing convenience for Internet business and resource docking, etc., and saving a lot of time and financial costs.

Description

[0001] This application is a divisional application of the Chinese invention patent application with the application date of July 27, 2021, application number CN202110848292.4, and invention name “Digital file information entity labeling and identification method, device and system”. Technical Field

[0002] The present invention relates to the field of artificial intelligence technology, applied to the direction of Internet technology, and in particular to a method, device and system for labeling and identifying cardiovascular disease information entities. Background Art

[0003] With the popularization of Internet technology, more and more applications have emerged. Internet + applications have become an effective means for people and society to obtain more equal and convenient medical services. The recognition of digital files has evolved from manual to automatic text recognition to the application of artificial intelligence technology. Due to the extremely high requirements for professional knowledge personnel, it requires not only professional medical knowledge but also algorithm or development knowledge. Therefore, inconvenience will greatly increase the burden. Summary of the invention

[0004] In view of the above-mentioned defects, the technical problem to be solved by the present invention is how to use artificial intelligence technology and natural language processing technology to perceive and identify various information of users and model the subsequent decision-making process to achieve automatic execution and intelligent decision-making.

[0005] In view of the above-mentioned defects, the object of the present invention is to provide a method, system and electronic equipment, computer storage medium and program product for labeling and identifying cardiovascular disease information entities.

[0006] Applied to the server side, it extracts full-text information from digital files or collects data that is ready for word segmentation, inputs the data into the labeling function, performs word segmentation training on the information based on regular matching and generates labels, and integrates the label data and the original data according to the model input parameters, and then inputs them into the model for entity recognition model training to produce a result set and corresponding scoring results.

[0007] Preferably, tasks are created according to requirements, associated training models are used, and computing space is allocated before uploading the data set to be labeled.

[0008] Preferably, the plain text is processed into data quadruple data in a table through a vocabulary, keyword, entity type, location and text subscript.

[0009] Preferably, the data is used as input to the labeling function, and an entity is generated for each row of data after training the Snorkel model.

[0010] Preferably, specifically including:

[0011] S1. extracting full text information from the text material input by the user;

[0012] S2, performing word segmentation processing on the text information;

[0013] S3, through the vocabulary, process the plain text into four-tuple data in the table;

[0014] S4, associate the Snorkel training model and allocate computing space;

[0015] S5. Upload the data set that needs to be labeled;

[0016] S6, generate label function and perform model training;

[0017] S7, output corresponding entities for the input data through Snorkel training;

[0018] S8. Merge the labeled data with the original data to generate Bert training data.

[0019] Preferably, the digital file is a coronary angiography report sheet and / or a coronary angiography case report.

[0020] Preferably, including:

[0021] S201, extracting full text information from the coronary angiography report sheet and / or the coronary angiography case report by OCR, including the coronary angiography case number;

[0022] S202, through the vocabulary, processing the plain text into data quadruple data in the table, labeled 1, 2, ..., the vocabulary is left anterior descending branch, right circumflex branch, ...;

[0023] S203, using the data obtained in step S202 as the input of the label function, and generating an entity for each row of data after training with Snorkel, wherein the coronary angiography report corresponds to the right coronary artery label, and the coronary angiography medical record number corresponds to the conventional angiography label;

[0024] S204: Fuse the labeled data with the original data to generate Bert training data.

[0025] The present invention provides a cardiovascular disease information entity labeling and recognition method, which is applied to an Internet medical platform, obtains user authorization based on a terminal device, collects digital files uploaded by the user and sends them to a data center processing system of a background server, performs OCR recognition on the digital files to obtain full-text information or collects data prepared for word segmentation, inputs the data into a label function, performs word segmentation training on the information based on regular matching and generates labels, integrates the label data and the original data according to the input parameters of the model, and inputs the data into the model for entity recognition model training to output a result set and corresponding scoring results, and outputs specific solutions to users in combination with application products of the Internet medical platform.

[0026] Preferably, the Internet medical platform combines the result set and the corresponding scoring results through a diagnostic decision model, uses a rule engine to simulate the insurance claim review, performs decision tree logic execution, and obtains and outputs intelligent review conclusions to the user.

[0027] Preferably, the Internet medical platform calculates the recommendation decision factors based on the result set and the corresponding scoring results, makes decisions through factor calculation, and uses the recommendation algorithm to derive the recommended insurance combination.

[0028] Preferably, the Internet medical platform combines the result set and the corresponding scoring results based on the rehabilitation data model, and provides relevant rehabilitation equipment and recommended solutions through model calculation.

[0029] Preferably, the Internet medical platform combines the result set and the corresponding scoring results with the national hospital big data database to give recommended treatments or connect with medical experts.

[0030] Preferably, the Internet medical platform creates tasks according to demand, associates training models, allocates computing space, and then uploads the data set to be labeled.

[0031] The present invention provides a cardiovascular disease information entity labeling and recognition system, comprising at least one terminal device, at least one Internet medical platform and at least one server, wherein the terminal device collects digital files uploaded by users, the Internet medical platform obtains user authorization based on the terminal device, collects digital files uploaded by users and sends them to a data center processing system of a background server, the data center processing system performs OCR recognition on the digital files to obtain full-text information or collects data prepared for word segmentation, inputs the data into a label function, performs word segmentation training on the information based on regular matching and generates labels, integrates the label data and the original data according to the input parameters of the model, and then inputs them into the model for entity recognition model training to produce a result set and corresponding scoring results, and outputs specific solutions to users in combination with application products of the Internet medical platform.

[0032] Preferably, tasks are created according to requirements, associated training models are used, and computing space is allocated before uploading the data set to be labeled.

[0033] Preferably, the plain text is processed into data quadruple data in a table through a vocabulary, keyword, entity type, location and text subscript.

[0034] The present invention provides a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the above method are implemented.

[0035] The present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0036] The present invention provides an electronic device, comprising:

[0037] Processor; and

[0038] A memory configured to store computer executable instructions which, when executed, cause the processor to:

[0039] The terminal device obtains user authorization and permission, and the terminal device collects digital files uploaded by the user. The Internet medical platform obtains user authorization and permission based on the terminal device, collects digital files uploaded by the user and sends them to the data center processing system of the background server. The data center processing system performs OCR recognition on the digital files to obtain full-text information or collects data prepared for word segmentation, inputs the data into the label function, performs word segmentation training on the information based on regular matching and generates labels, integrates the label data and the original data according to the input parameters of the model, and inputs them into the model for entity recognition model training to produce a result set and corresponding scoring results, and outputs specific solutions to users in combination with the application products of the Internet medical platform.

[0040] The present invention solves the timeliness and cost issues of information entity labeling for massive digital files through model training, and allows non-algorithm personnel to quickly implement operations through program implementation. This is a great innovation in the tool category and can be widely used in data labeling in the medical and health field, providing convenience for medical insurance, fitness, and medical resource docking, saving a lot of time and financial costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the following briefly introduces the drawings required for use in the embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1A schematic diagram showing the structure of an embodiment of a method for labeling and identifying cardiovascular disease information entities according to the present invention is shown;

[0043] Figure 2 A schematic structural diagram of another embodiment of the cardiovascular disease information entity labeling and identification method of the present invention is shown;

[0044] Figure 3 A schematic structural diagram of another embodiment of the cardiovascular disease information entity labeling and identification method of the present invention is shown;

[0045] Figure 4 A schematic diagram of another embodiment of the cardiovascular disease information entity labeling and identification method of the present invention is shown;

[0046] Figure 5 A schematic diagram of the flow chart of an embodiment of a cardiovascular disease information entity labeling and recognition system of the present invention is shown. DETAILED DESCRIPTION

[0047] The features and exemplary embodiments of various aspects of the present invention will be described in detail below. In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only configured to explain the present invention and are not configured to limit the present invention. For those skilled in the art, the present invention can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present invention by illustrating examples of the present invention.

[0048] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0049] An embodiment of the present specification provides a method for labeling and identifying cardiovascular disease information entities, extracts full-text information from digital files or collects data prepared for word segmentation, inputs the data into a label function, performs word segmentation training on the information based on regular matching and generates labels, integrates the label data and the original data according to the model input parameters, and inputs the data into the model for entity recognition model training to produce a result set and corresponding scoring results.

[0050] In some embodiments, tasks are created based on demand, associated with training models, and the data set to be labeled is uploaded after computing space is allocated.

[0051] In some embodiments, plain text is processed through a vocabulary into data quadruple data in a table, keyword, entity type, location, and text subscript.

[0052] In some embodiments, the data is used as input to a labeling function, and an entity is generated for each row of data after training the Snorkel model.

[0053] In the category of mental illness, many mental illnesses and sleep disorders are caused by cardiovascular reasons. Taking cardiovascular coronary arteries as an example, the digital files are coronary angiography report sheets and / or coronary angiography case reports.

[0054] like Figure 1 As shown, an embodiment of this specification provides a method for labeling and identifying cardiovascular disease information entities, including:

[0055] S101, extracting full text information from a digital file or collecting data ready for word segmentation;

[0056] S102, inputting data into a label function;

[0057] S103, performing word segmentation training on the information based on regular matching and generating labels;

[0058] S104, integrating the label data and the original data according to the input parameters of the model;

[0059] S105, input into the model for entity recognition model training to produce a result set and corresponding scoring results.

[0060] like Figure 2 As shown, this specification provides an embodiment of a method for labeling and identifying cardiovascular disease information entities, including:

[0061] S201, extracting full text information from the coronary angiography report sheet and / or the coronary angiography case report by OCR, including the coronary angiography case number;

[0062] S202, through the vocabulary, processing the plain text into data quadruple data in the table, labeled 1, 2, ..., the vocabulary is left anterior descending branch, right circumflex branch, ...;

[0063] S203, using the data obtained in step S202 as the input of the label function, and generating an entity for each row of data after training with Snorkel, wherein the coronary angiography report corresponds to the right coronary artery label, and the coronary angiography medical record number corresponds to the conventional angiography label;

[0064] S204: Fuse the labeled data with the original data to generate Bert training data.

[0065] like Figure 3 As shown, this specification provides an embodiment of a method for labeling and identifying cardiovascular disease information entities, specifically comprising:

[0066] S1. extracting full text information from the text material input by the user;

[0067] S2, performing word segmentation processing on the text information;

[0068] S3, through the vocabulary, process the plain text into four-tuple data in the table;

[0069] S4, associate the Snorkel training model and allocate computing space;

[0070] S5. Upload the data set that needs to be labeled;

[0071] S6, generate label function and perform model training;

[0072] S7, output corresponding entities for the input data through Snorkel training;

[0073] S8. Merge the labeled data with the original data to generate Bert training data.

[0074] The present specification provides an embodiment of a method for labeling and identifying cardiovascular disease information entities, which is applied to an Internet medical platform, obtains user authorization based on a terminal device, collects digital files uploaded by the user and sends them to a data center processing system of a backend server, performs OCR recognition on the digital files to obtain full-text information or collects data prepared for word segmentation, inputs the data into a label function, performs word segmentation training on the information based on regular matching and generates labels, integrates the label data and the original data according to the input parameters of the model, and inputs the data into the model for entity recognition model training to produce a result set and corresponding scoring results, and outputs specific solutions to users in combination with the application products of the Internet medical platform.

[0075] In some embodiments, the Internet medical platform combines the result set and the corresponding scoring results through a diagnostic decision model, uses a rule engine to simulate insurance claims review, performs decision tree logic execution, and obtains and outputs intelligent review conclusions to users.

[0076] In some embodiments, the Internet medical platform calculates the recommendation decision factors based on the result set and the corresponding scoring results, calculates the decision through the factors, and uses the recommendation algorithm to derive the insurance recommendation combination.

[0077] In some embodiments, the Internet medical platform combines the result set and the corresponding scoring results based on the rehabilitation data model, and provides relevant rehabilitation equipment and recommended solutions through model calculation.

[0078] In some embodiments, the Internet medical platform combines the result set and the corresponding scoring results with the national hospital database to recommend treatment methods or connect with medical experts.

[0079] In some embodiments, the Internet medical platform creates tasks based on demand, associates training models, allocates computing space, and then uploads the data set to be labeled.

[0080] like Figure 4 As shown, the present specification provides an embodiment of a cardiovascular disease information entity labeling and identification system, including at least one terminal device, at least one Internet medical platform and at least one server, wherein the terminal device collects digital files uploaded by users, and the Internet medical platform obtains user authorization based on the terminal device, collects digital files uploaded by users and sends them to a data center processing system of a background server, and the data center processing system performs OCR recognition on the digital files to obtain full-text information or collects data prepared for word segmentation, inputs the data into a label function, performs word segmentation training on the information based on regular matching and generates labels, and integrates the label data and the original data according to the input parameters of the model, and then inputs them into the model for entity recognition model training to produce a result set and corresponding scoring results, and outputs specific solutions to users in combination with the application products of the Internet medical platform.

[0081] In some embodiments, tasks are created based on demand, associated with training models, and the data set to be labeled is uploaded after computing space is allocated.

[0082] In some embodiments, plain text is processed through a vocabulary into data quadruple data in a table, keyword, entity type, location, and text subscript.

[0083] In a specific example, insurance includes health insurance and life insurance, and various insurances include health condition disclosure.

[0084] In a specific example, corresponding rehabilitation equipment or suggestions are given through a recommendation algorithm model and imported into corresponding third-party e-commerce platforms, such as Ali Health, JD Health, etc.

[0085] In a specific example, the result set, the corresponding scoring results and the national hospital big data database are combined to give recommended treatments or connect with medical experts, including hospitals or doctors on online medical platforms such as Ali Health, Dingxiangyuan, and Chunyu Doctor.

[0086] In some specific examples, such as neurasthenia, sleep disorders, cardiovascular diseases, etc., electrocardiograms, health pillows, melatonin and other products or health products that help relieve stress and improve sleep quality can be provided.

[0087] In some specific examples, the system processes digital files uploaded by users including, but not limited to, diagnosis certificates, case reports, prescriptions, and hospitalization records.

[0088] An embodiment of the present specification provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the following method when executed by a processor: obtaining user authorization based on a terminal device, collecting digital files uploaded by the user and sending them to a data center processing system of a backend server, the data center processing system performing OCR recognition on the digital files to obtain full-text information or collecting data prepared for word segmentation, inputting the data into a labeling function, performing word segmentation training on the information based on regular matching and generating labels, integrating the label data and the original data according to the input parameters of the model, and inputting the label data into the model for entity recognition model training to produce a result set and corresponding scoring results, and outputting specific solutions to users in combination with the application products of the Internet medical platform.

[0089] A computer program product provided by an embodiment of the present specification includes a computer program / instruction, which implements the following method when executed by a processor: obtaining user authorization based on a terminal device, collecting digital files uploaded by the user and sending them to a data center processing system of a background server, the data center processing system performing OCR recognition on the digital files to obtain full-text information or collecting data prepared for word segmentation, inputting the data into a labeling function, performing word segmentation training on the information based on regular matching and generating labels, integrating the label data and the original data according to the input parameters of the model, and inputting the data into the model for entity recognition model training to produce a result set and corresponding scoring results, and outputting specific solutions to users in combination with the application products of the Internet medical platform.

[0090] An electronic device provided by an embodiment of the present specification includes:

[0091] Processor; and

[0092] A memory configured to store computer executable instructions which, when executed, cause the processor to:

[0093] Obtain user authorization license based on terminal devices, collect digital files uploaded by users and send them to the data center processing system of the background server, perform OCR recognition on digital files to obtain full-text information or collect data prepared for word segmentation, input the data into the label function, perform word segmentation training on the information based on regular matching and generate labels, integrate the label data and the original data according to the model input parameters, input the label data into the model for entity recognition model training, output the result set and the corresponding scoring results, and output specific solutions to users in combination with the application products of the Internet medical platform.

[0094] The present invention solves the timeliness and cost issues of information entity labeling for massive digital files through model training, and allows non-algorithm personnel to quickly implement operations through program implementation. This is a great innovation in the tool category and can be widely used in data labeling in the medical and health field, providing convenience for medical insurance, fitness, and medical resource docking, saving a lot of time and financial costs.

[0095] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0096] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0099] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0101] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0102] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0103] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0104] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0105] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0106] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for labeling and identifying cardiovascular disease information entities, extracting full text information from a coronary angiography report sheet and / or a coronary angiography case report input by a user through OCR and performing word segmentation on the text information or collecting data prepared for word segmentation, processing the plain text into four-tuple data in a table through a vocabulary, associating a Snorkel training model and allocating computing space, uploading a data set to be labeled, generating a label function and performing model training, outputting corresponding entities for the input data through Snorkel training, fusing the labeled data with the original data, generating Bert training data, and inputting the data into the model for entity recognition. The model training outputs a result set and a corresponding scoring result, specifically including: S1. extracting full text information from the text material input by the user; S2, performing word segmentation processing on the text information; S3, through the vocabulary, process the plain text into four-tuple data in the table; S4, associate the Snorkel training model and allocate computing space; S5. Upload the data set that needs to be labeled; S6, generate label function and perform model training; S7, output corresponding entities for the input data through Snorkel training; S8. Merge the labeled data with the original data to generate Bert training data.

2. The cardiovascular disease information entity labeling and identification method according to claim 1, characterized in that: The method creates tasks according to requirements, associates training models, allocates computing space, and then uploads the data set to be labeled.

3. The cardiovascular disease information entity labeling and identification method according to claim 1, characterized in that: The plain text is processed into four-tuple data in a table through a vocabulary, including keywords, entity types, positions, and text subscripts.

4. The cardiovascular disease information entity labeling and identification method according to any one of claims 1 to 3, characterized in that: The data is used as the input of the label function, and an entity is generated for each row of data after training the Snorkel model.

5. The cardiovascular disease information entity labeling and identification method according to claim 4, characterized in that: The method comprises: S201, extracting full text information from the coronary angiography report sheet and / or the coronary angiography case report by OCR, including the coronary angiography case number; S202, processing the plain text into four-tuple data in a table through a vocabulary; S203, using the data obtained in step S202 as the input of the label function, and generating an entity for each row of data after training with Snorkel, wherein the coronary angiography report corresponds to the right coronary artery label, and the coronary angiography medical record number corresponds to the conventional angiography label; S204: Fuse the labeled data with the original data to generate Bert training data.

6. A cardiovascular disease information entity labeling and recognition method, applied to an Internet medical platform, obtains user authorization based on a terminal device, collects coronary angiography report sheets and / or coronary angiography case reports uploaded by users and sends them to a data center processing system of a backend server, the data center processing system performs OCR recognition on the coronary angiography report sheets and / or coronary angiography case reports to obtain full-text information or collects data prepared for word segmentation, processes plain text into four-tuple data in a table through a vocabulary, processes plain text into four-tuple data in a table through a vocabulary, associates a Snorkel training model and allocates computing space, uploads a data set to be labeled, generates a label function and performs model training, outputs corresponding entities to the input data through Snorkel training, merges the labeled data with the original data, generates Bert training data, and inputs them into the model for entity recognition. The model training outputs a result set and a corresponding scoring result, and outputs a specific solution to the user in combination with the application products of the Internet medical platform, specifically including: S1. extracting full text information from the text material input by the user; S2, performing word segmentation processing on the text information; S3, through the vocabulary, process the plain text into four-tuple data in the table; S4, associate the Snorkel training model and allocate computing space; S5. Upload the data set that needs to be labeled; S6, generate label function and perform model training; S7, output corresponding entities for the input data through Snorkel training; S8. Merge the labeled data with the original data to generate Bert training data.

7. The method according to claim 6, characterized in that The Internet medical platform combines the result set and the corresponding scoring results based on the rehabilitation data model, and provides relevant rehabilitation equipment and electrocardiographs, health pillows, melatonin or other products or health products through model calculation.

8. The method according to claim 6, characterized in that The Internet medical platform creates tasks according to needs, associates training models, allocates computing space, and then uploads the data set to be labeled.

9. A computer-readable storage medium having a computer program / instruction stored thereon, which when executed by a processor implements the steps of the method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program / instructions, which when executed by a processor implements the steps of the method according to any one of claims 1 to 5.

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