Medical data structuring method and device, equipment and medium

Abstract

The invention discloses a medical data structuring method and device, equipment and a medium, and the method comprises the steps: obtaining a target medical data file and a standard structuring prompt template, carrying out the text detection and recognition of the target medical data file, obtaining the text information in the target medical data file and the corresponding position information, performing layout extraction on the target medical data file to obtain layout information corresponding to the target medical data file, and performing structured processing on text information, corresponding position information and layout information in the target medical data file to obtain initial structured data; and inputting the initial structured data and the standard structured prompt template into the standardized and trained model, and outputting the standard structured data. The standard structured processing of the medical data file is automatically realized, the accuracy of the obtained standard structured data is also improved, and data support is provided for downstream tasks of the medical data.

Description

Technical Field

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

[0002] With the development of medical technology, the amount and types of medical data are constantly increasing. These data cover text data such as medical records, medical orders and examination reports, image data such as X-rays and CT scans, and physiological data such as electrocardiograms, blood pressure and blood sugar. Medical structuring refers to the organization and standardization of medical data for storage, management and analysis. By converting medical data into a structured form, the readability, searchability and analyzability of the data can be improved, thereby providing better support for medical institutions and medical research. For example, in auxiliary / AI diagnosis scenarios, medical reports (such as physical examination reports, case reports, etc.) can be structured and analyzed to mine the patient's medical history and condition information and establish a disease prediction model to assist doctors in making diagnosis and treatment decisions; in the application scenarios of statistical analysis and clinical research, structured medical data can also help researchers count which populations and regions often have symptoms, diseases and applicable drugs for pathological research; in the field of business risk assessment, structured medical data can help insurance companies standardize and structure customer medical record information to facilitate analysis and assessment of customer risk costs, etc. However, most of the existing medical data structuring needs to be processed manually, which is very inefficient and the long processing time will also cause eye fatigue. Therefore, the manual method is no longer suitable for the current rapid development of computer and other fields.

[0003] Therefore, how to automatically realize the structured processing of medical data and provide support for downstream tasks of medical data has become an urgent problem to be solved. Summary of the invention

[0004] Based on this, a medical data structuring method, apparatus, device and medium are provided to solve the problem of how to automatically realize the structured processing of medical data and provide support for downstream tasks of medical data.

[0005] In a first aspect, an embodiment of the present invention provides a medical data structuring method, comprising: Obtaining target medical data files and standard structured prompt templates; Performing text detection and recognition on the target medical data file to obtain text information and corresponding position information in the target medical data file, and performing layout extraction on the target medical data file to obtain layout information corresponding to the target medical data file; Performing structural processing on the text information, corresponding position information and format information in the target medical data file to obtain initial structured data; The initial structured data and the standard structured prompt template are input into a model that has been trained after standardization processing, and the standard structured data is output.

[0006] In a second aspect, an embodiment of the present invention provides a medical data structuring device, comprising: A data acquisition module, used to acquire target medical data files and standard structured prompt templates; An information extraction module is used to perform text detection and recognition on the target medical data file to obtain text information and corresponding position information in the target medical data file, and perform layout extraction on the target medical data file to obtain layout information corresponding to the target medical data file; A preliminary structuring module, used for structuring the text information, corresponding position information and format information in the target medical data file to obtain initial structured data; The standard structured module is used to input the initial structured data and the standard structured prompt template into a model that has been trained for standardization processing, and output standard structured data.

[0007] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the medical data structuring method of the first aspect when executing the computer program.

[0008] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the medical data structuring method of the first aspect is implemented.

[0009] The technical effects achieved by the present invention are different from those achieved by the prior art solutions: the present invention performs text detection and recognition on the target medical data file to obtain the text information and the corresponding position information in the target medical data file, performs layout extraction on the target medical data file to obtain the layout information corresponding to the target medical data file, performs structured processing on the text information, the corresponding position information and the layout information in the target medical data file to obtain initial structured data, inputs the initial structured data and the standard structured prompt template into the model that has been trained for standardization processing, and outputs standard structured data. Among them, by performing initial information extraction on the target medical data file to obtain the initial structured data, the model that has been trained for standardization processing performs standard processing on the initial structured data based on the standard structured prompt template to obtain the standard structured data, and automatically realizes the standard structured processing of the medical data file, and the correction processing of the initial structured data based on the model and the prompt template also improves the accuracy of the obtained standard structured data, thereby providing data support for downstream tasks of medical data. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0011] Figure 1 This is a schematic diagram of an application environment of a medical data structuring method provided in Embodiment 1 of the present invention; Figure 2 is a flowchart of a medical data structuring method provided in Embodiment 2 of the present invention; Figure 3 is a flowchart of a medical data structuring method provided in Embodiment 3 of the present invention; Figure 4 is a schematic diagram of the structure of a medical data structuring device provided by a fourth embodiment of the present invention; Figure 5 It is a structural diagram of a computer device provided in Embodiment 5 of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0014] A medical data structuring method provided in the first embodiment of the present invention can be applied in the following aspects: Figure 1 In the application environment, the server communicates with the client, the server provides medical data structuring services, and the client triggers medical data structuring tasks to the server. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud computing devices, personal digital assistants (PDAs), and other devices. The computer device corresponding to the server can be implemented with an independent server or a server cluster consisting of multiple servers.

[0015] like Figure 2 FIG. 1 is a flow chart of a medical data structuring method provided in Embodiment 2 of the present invention, comprising the following steps: Step S201: Acquire a target medical data file and a standard structured prompt template.

[0016] In this embodiment, the target medical data file may refer to a file to be structured, for example, the target medical data file may be a physical examination report or a case report, and the standard structured prompt template may refer to standard structured information customized for structuring the target medical data file.

[0017] For example, the standard structured prompt template could be: To normalize your initial structured data, follow these guidelines: 1. Keep all the original headings and convert them to the appropriate marked heading levels (# for main heading, ## for secondary, etc.), making sure each heading is on its own line, and add a blank line before and after each heading; 2. If the list exists in the original text, format it correctly (unordered or ordered); 3. Preserve all original content and meaning; 4. Remove any spurious inserted introductory text, such as “This is the corrected text:”, which may have been added by a large language model and is obviously not part of the original text; 5. Remove any obvious duplicate content that appears to have been accidentally included twice. Follow these strict guidelines: - Remove only exact or nearly exact duplicate paragraphs or sections of the main block.

[0018] - Consider the context (before and after the main chunk) to identify duplicates that cross chunk boundaries.

[0019] -Don’t delete content that is simply similar but conveys a different message.

[0020] - Keep all unique content, even if it seems redundant.

[0021] - Make sure the text flows smoothly after deletion.

[0022] -Don't add anything new or explain anything new.

[0023] - If no obvious duplicates are found, the main block is returned unchanged.

[0024] 6. Identify but do not remove headers, footers, or page numbers. Instead, format them explicitly; 7. Try to reconstruct the table based on the image or correct the incorrect table reconstruction content (if any).

[0025] Step S202: Perform text detection and recognition on the target medical data file to obtain text information and corresponding position information in the target medical data file, and perform layout extraction on the target medical data file to obtain layout information corresponding to the target medical data file.

[0026] Step S203: Structuring the text information, corresponding position information and format information in the target medical data file to obtain initial structured data.

[0027] In this embodiment, the initial structured data may refer to structured data obtained by performing preliminary structured processing on the target medical data file.

[0028] Specifically, in the process of performing text detection and recognition on the target medical data file to obtain the text information and corresponding position information in the target medical data file, firstly, the text area in the target medical data file is determined by the text area detection technology, and the detected text area is divided into single characters, and then the feature information of each character is extracted, and the text information in the text area is determined according to the extracted character features by the character recognition technology, and the specific position information of the text in the target medical data file is determined during the recognition process.

[0029] In the process of performing layout extraction on the target medical data file and obtaining the layout information corresponding to the target medical data file, first, the target medical data file is input into the model that has been trained for layout analysis, and the layout format information corresponding to the target medical data file is output, such as double columns and triple columns, etc. Then, the target medical data file is input into the model that has been trained for table extraction, and the table information is output, such as a bordered table / borderless table, etc. Finally, the layout format information and the table information are combined to obtain the layout information.

[0030] And preliminary structural processing is performed based on the extracted text information, position information and layout information to obtain initial structured data.

[0031] Step S204: input the initial structured data and the standard structured prompt template into the model that has been trained for standardization processing, and output the standard structured data.

[0032] In this embodiment, standard structured data may refer to structured data that is obtained by standardizing initial structured data according to a standard structured prompt template, and the model that has been trained through standardization may refer to a deep learning model that has been trained through standardization for multimodal tasks. The model can integrate multiple modalities such as vision and text, and can process and understand various types of data inputs, such as images and text.

[0033] Specifically, the text information, position information and layout information in the initial structured data are adjusted according to the standard structured prompt template through the model that has been trained for standardization processing to obtain adjusted text information, adjusted position information, adjusted layout information and corresponding adjustment scores, and the adjusted text information, adjusted position information and adjusted layout information are formed into candidate structured data, and the adjustment score is used as the candidate score of the candidate structured data. According to the candidate score, all candidate structured data are sorted, and the candidate structured data with the highest candidate score is output as the standard structured data.

[0034] In this embodiment, initial structured data is obtained by performing initial information extraction on the target medical data file, so that the model that has been trained for standardization performs standardization on the initial structured data based on a standard structured prompt template to obtain standard structured data, thereby automatically realizing standard structured processing of medical data files, and correcting the initial structured data based on the model and prompt template, thereby improving the accuracy of the obtained standard structured data, thereby providing data support for downstream tasks of medical data.

[0035] like Figure 3FIG. 2 is a flow chart of a medical data structuring method provided in Embodiment 3 of the present invention. After outputting the standard structured data in the above step S204, the following steps may also be included: Step S301: perform a redundancy check on the standard structured data to obtain a check result.

[0036] Step S302: Optimize the parameters of the model that has been trained through the standardization process according to the inspection results to obtain an updated model.

[0037] Step S303: input the initial structured data and the standard structured prompt template into the updated model, and output the updated structured data.

[0038] Specifically, after the initial structured data and the standard structured prompt template are input into the model that has been standardized and trained, and the standard structured data is output, the content of the standard structured data can also be checked, and logical judgment is made whether there is redundancy in the standard structured data. If there is redundancy, the parameters of the model that has been standardized and trained are optimized to obtain an updated model, and the initial structured data and the standard structured prompt template are input into the updated model for re-examination, and the updated structured data is output.

[0039] Optionally, after outputting the updated structured data, similarity matching technology can be used to perform similarity matching on the text information in the standard structured data and the text information in the target medical data file to obtain a similarity score. If the similarity score meets a preset threshold, it is determined that the similarity verification of the standard structured data has passed. If the similarity score does not meet the preset threshold, the standard structured data can be marked for subsequent manual verification.

[0040] Optionally, after outputting the updated structured data, the medical data items in the standard structured data can also be determined, and the medical data items can be matched with the standard medical data items in a preset medical database. If the match succeeds, it is determined that the medical data items of the standard structured data have been verified. If the match fails, the standard structured data can be marked for subsequent manual verification.

[0041] In this embodiment, by inputting the initial structured data and the standard structured prompt template into a model that has been trained for standardization processing, and then outputting the standard structured data, redundancy checking, similarity matching and medical data item matching are performed on the standard structured data, thereby improving the accuracy of the obtained standard structured data.

[0042] like Figure 4As shown, a medical data structuring device provided in the fourth embodiment of the present invention corresponds to the medical data structuring method in the above embodiment. The medical data structuring device includes a data acquisition module 41, an information extraction module 42, a preliminary structuring module 43 and a standard structuring module 44. The functional modules are described in detail as follows: A data acquisition module 41 is used to acquire a target medical data file and a standard structured prompt template; An information extraction module 42 is used to perform text detection and recognition on the target medical data file to obtain text information and corresponding position information in the target medical data file, and perform layout extraction on the target medical data file to obtain layout information corresponding to the target medical data file; A preliminary structuring module 43 is used to perform structural processing on the text information, corresponding position information and format information in the target medical data file to obtain initial structured data; The standard structured module 44 is used to input the initial structured data and the standard structured prompt template into a model that has been trained for standardization processing, and output standard structured data.

[0043] Optionally, the information extraction module 42 includes: A text region detection unit, used to determine the text region in the target medical data file by using a text region detection technology; The character recognition unit is used to perform text detection and recognition on the text area through character recognition technology to obtain the text information and the position information.

[0044] Optionally, the information extraction module 42 includes: A layout extraction unit, used for inputting the target medical data file into a model that has been trained for layout analysis, and outputting layout format information corresponding to the target medical data file; A table extraction unit, used for inputting the target medical data file into a model that has been trained for table extraction, and outputting table information; A combining unit is used to combine the layout format information and the table information to obtain the format information.

[0045] Optionally, the above standard structured module 44 includes: An adjustment unit, configured to adjust the text information, position information and layout information in the initial structured data according to the standard structured prompt template by using the model that has been trained through the standardization process, so as to obtain adjusted text information, adjusted position information, adjusted layout information and a corresponding adjustment score; A candidate unit, configured to form candidate structured data from the adjusted text information, the adjusted position information and the adjusted layout information, and use the adjusted score as a candidate score of the candidate structured data; The sorting unit is used to sort all the candidate structured data according to the candidate scores, and output the candidate structured data with the highest candidate score as the standard structured data.

[0046] Optionally, the medical data structuring device further includes: A redundancy check module, used to perform a redundancy check on the standard structured data to obtain a check result; A model optimization module, used to optimize the parameters of the model that has been trained through the standardization process according to the inspection results to obtain an updated model; The updating module is used to input the initial structured data and the standard structured prompt template into the updated model and output updated structured data.

[0047] Optionally, the medical data structuring device further includes: A similarity matching module, used to perform similarity matching on the text information in the standard structured data and the text information in the target medical data file through similarity matching technology to obtain a similarity score; The similarity verification module is used to determine that the similarity verification of the standard structured data passes if the similarity score meets a preset threshold.

[0048] Optionally, the medical data structuring device further includes: The data item verification module is used to determine the medical data items in the standard structured data, match the medical data items with the standard medical data items, and if the match succeeds, determine that the medical data items of the standard structured data have passed the verification.

[0049] For the specific definition of the medical data structuring device, please refer to the definition of the medical data structuring method above, which will not be repeated here. Each module in the above-mentioned medical data structuring device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0050] Figure 5 This is a schematic diagram of the structure of a computer device provided in Embodiment 5 of the present invention. Figure 5 As shown, the computer device of this embodiment includes: at least one processor ( Figure 5Only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor, which implements the steps of any of the above-mentioned medical data structuring method embodiments when the processor executes the computer program.

[0051] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 5 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0052] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0053] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0054] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0055] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.

[0056] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0057] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can 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 the present invention.

[0058] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0059] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0060] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents, and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for structuring medical data, characterized in that: include: Obtaining target medical data files and standard structured prompt templates; Performing text detection and recognition on the target medical data file to obtain text information and corresponding position information in the target medical data file, and performing layout extraction on the target medical data file to obtain layout information corresponding to the target medical data file; Performing structural processing on the text information, corresponding position information and format information in the target medical data file to obtain initial structured data; The initial structured data and the standard structured prompt template are input into a model that has been trained after standardization processing, and the standard structured data is output.

2. The medical data structuring method according to claim 1, characterized in that: The performing text detection and recognition on the target medical data file to obtain text information and corresponding position information in the target medical data file includes: Determining the text region in the target medical data file by using text region detection technology; The text area is subjected to text detection and recognition by character recognition technology to obtain the text information and the position information.

3. The medical data structuring method according to claim 1, characterized in that: The step of extracting the layout of the target medical data file to obtain layout information corresponding to the target medical data file includes: Inputting the target medical data file into a model that has been trained for layout analysis, and outputting layout format information corresponding to the target medical data file; Inputting the target medical data file into a model that has been trained for table extraction, and outputting table information; The layout format information and the table information are combined to obtain the format information.

4. The medical data structuring method according to claim 1, characterized in that: The step of inputting the initial structured data and the standard structured prompt template into a model that has been trained for standardization processing, and outputting the standard structured data, comprises: The model that has been trained through standardization is used to adjust the text information, position information and layout information in the initial structured data according to the standard structured prompt template to obtain adjusted text information, adjusted position information, adjusted layout information and corresponding adjustment scores; The adjusted text information, the adjusted position information and the adjusted layout information form candidate structured data, and the adjusted score is used as a candidate score of the candidate structured data; All candidate structured data are sorted according to the candidate scores, and the candidate structured data with the highest candidate score is output as the standard structured data.

5. The medical data structuring method according to claim 1, characterized in that: After outputting the standard structured data, the method further includes: Performing a redundancy check on the standard structured data to obtain a check result; Optimizing parameters of the model that has been trained through the standardization process according to the inspection results to obtain an updated model; The initial structured data and the standard structured prompt template are input into the updated model, and updated structured data is output.

6. The medical data structuring method according to claim 1, characterized in that: After outputting the standard structured data, the method further includes: By using similarity matching technology, similarity matching is performed on the text information in the standard structured data and the text information in the target medical data file to obtain a similarity score; If the similarity score meets a preset threshold, it is determined that the similarity verification of the standard structured data has passed.

7. The medical data structuring method according to claim 1, characterized in that: After outputting the standard structured data, the method further includes: The medical data items in the standard structured data are determined, and the medical data items are matched with the standard medical data items. If the match succeeds, it is determined that the medical data items of the standard structured data have passed the verification.

8. A medical data structuring device, characterized in that: include: A data acquisition module, used to acquire target medical data files and standard structured prompt templates; An information extraction module is used to perform text detection and recognition on the target medical data file to obtain text information and corresponding position information in the target medical data file, and to perform layout extraction on the target medical data file to obtain layout information corresponding to the target medical data file; A preliminary structuring module, used for structuring the text information, corresponding position information and format information in the target medical data file to obtain initial structured data; The standard structured module is used to input the initial structured data and the standard structured prompt template into a model that has been trained for standardization processing, and output standard structured data.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the medical data structuring method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the medical data structuring method according to any one of claims 1 to 7 is implemented.

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