Method, device and equipment for generating orthopedic electronic health record based on orthopedic clinical medical record document

Abstract

The invention discloses a method, device and equipment for generating an orthopedic electronic health record based on an orthopedic clinical medical record document, and relates to the field of medical data arrangement. According to the method, the document in the picture format and the document containing the text information and the table information at the same time in the orthopedic clinical medical record document are recognized respectively, aiming at the defects of the OCR algorithm in the table information recognition process, the table area in the document is enhanced firstly, then the OCR algorithm is used for recognition, and the recognition efficiency of the table information is improved. In addition, before the orthopedic electronic health record is constructed, the first identification information, the second identification information and the medical image data are subjected to de-identification processing, so that the privacy of patient data is ensured.

Description

Technical Field

[0001] The present application relates to the field of medical data collation, and in particular to a method, device and equipment for generating orthopedic electronic health records based on orthopedic clinical medical record documents. Background Art

[0002] A large amount of electronic health records (EHR) data has been collected from medical institutions, including medical images, measurement data, physician notes, treatment records, demographic data, etc. These data contain valuable information to assess the clinical status of patients and their response to treatment, providing promising opportunities for clinical research. Typically, these data are stored in document formats, are not structured for interpretation and analysis, and do not support research purposes. This paper constructs an orthopedic medical benchmark dataset from unstructured archives. There are two major challenges: (1) the conversion from documents to EHR datasets usually requires high-quality optical character recognition (OCR) technology to extract important information; (2) it is challenging to share patient data using EHR systems in medical environments. Although OCR technology is widely used to extract information from hospital archives in the form of unstructured digital documents (such as portable document formats and images), it is difficult to parse special format data (such as tabular data) into a structured machine-readable format due to the complexity of their structure and style. Figure 1 An example of recognition results using Baidu's advanced OCR tool PP-Structure is shown. Obviously, the tool failed to accurately parse the hospital document, with multiple errors in words and formatting. Summary of the invention

[0003] The purpose of this application is to provide a method, device and equipment for generating orthopedic electronic health records based on orthopedic clinical medical record documents, which can improve the accuracy of table data recognition in orthopedic clinical medical record documents and ensure the privacy and security of patient data.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a method for generating an orthopedic electronic health record based on an orthopedic clinical medical record document, comprising:

[0006] Obtaining an orthopedic clinical medical record document; the orthopedic clinical medical record document includes: a first medical record document image and a second medical record document image, the first medical record document image and the second medical record document image are both medical record documents in image format, the first medical record document image only contains text information, and the second medical record document image contains both text information and table information;

[0007] Use the PaddleOCR algorithm to extract text information from the first medical record document image as the first recognition information;

[0008] Use OpenCV algorithm to divide the text area and table area in the second medical record document image;

[0009] Use a line segment detection algorithm to enhance the table area to obtain an enhanced table area;

[0010] Use the PaddleOCR algorithm to extract text information in the text area, and use the PP-Structure algorithm to extract text information in the enhanced table area to obtain second recognition information;

[0011] Acquiring medical imaging data corresponding to the orthopedic clinical medical record document;

[0012] De-identifying the first identification information, the second identification information, and the medical image data;

[0013] An orthopedic electronic health record is generated based on the de-identified first identification information, the second identification information and the medical imaging data.

[0014] In a second aspect, the present application provides a device for generating an orthopedic electronic health record based on an orthopedic clinical medical record document. The device for generating an orthopedic electronic health record based on an orthopedic clinical medical record document applies the above-mentioned method for generating an orthopedic electronic health record based on an orthopedic clinical medical record document. The device for generating an orthopedic electronic health record based on an orthopedic clinical medical record document includes:

[0015] A medical record document acquisition module, used to acquire orthopedic clinical medical record documents; the orthopedic clinical medical record documents include: a first medical record document image and a second medical record document image, the first medical record document image and the second medical record document image are both medical record documents in image format, the first medical record document image only contains text information, and the second medical record document image contains both text information and table information;

[0016] A first text information extraction module, used to extract text information from the first medical record document image using a PaddleOCR algorithm as first identification information;

[0017] A region division module, used for dividing the text region and the table region in the second medical record document image by using an OpenCV algorithm;

[0018] A table enhancement module is used to enhance the table area using a line segment detection algorithm to obtain an enhanced table area;

[0019] A second text information extraction module is used to extract text information in the text area using the PaddleOCR algorithm, and to extract text information in the enhanced table area using the PP-Structure algorithm to obtain second recognition information;

[0020] A medical image data acquisition module, used to acquire medical image data corresponding to the orthopedic clinical medical record document;

[0021] A de-identification module, used for de-identifying the first identification information, the second identification information and the medical image data;

[0022] The orthopedic electronic health record generation module is used to generate an orthopedic electronic health record based on the de-identified first identification information, the second identification information and the medical imaging data.

[0023] In a third aspect, the present application 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 executes the computer program to implement the above-mentioned method of generating an orthopedic electronic health record based on orthopedic clinical medical record documents.

[0024] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0025] The present application provides a method, device and equipment for generating orthopedic electronic health records based on orthopedic clinical medical record documents. The present application respectively identifies the first medical record document image containing only text information and the second medical record document image containing both text information and table information in the orthopedic clinical medical record document. In view of the shortcomings of the OCR algorithm in the recognition process of table information, the present application first enhances the table area in the document, and then uses the OCR algorithm for recognition, thereby improving the accuracy of table data recognition in orthopedic clinical medical record documents. In addition, before constructing the orthopedic electronic health record, the present application first de-identifies the first identification information, the second identification information and the medical imaging data to ensure the privacy of the patient data. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0027] Figure 1 This is an example diagram of the results of the OCR algorithm in the background technology of this application recognizing table data;

[0028] Figure 2A flowchart of a method for generating an orthopedic electronic health record based on an orthopedic clinical medical record document provided in one embodiment of the present application;

[0029] Figure 3 A schematic diagram of a method for generating an orthopedic electronic health record based on an orthopedic clinical medical record document provided in one embodiment of the present application;

[0030] Figure 4 A schematic diagram of the recognition result of the OCR algorithm before enhancement provided in one embodiment of the present application;

[0031] Figure 5 A schematic diagram of the recognition result of an enhanced OCR algorithm provided in an embodiment of the present application;

[0032] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0034] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0035] In an exemplary embodiment, Figure 2 and Figure 3 As shown, a method for generating an orthopedic electronic health record based on an orthopedic clinical medical record document is provided, comprising the following steps 201 to 208. Among them:

[0036] Step 201, obtain an orthopedic clinical medical record document; the orthopedic clinical medical record document includes: a first medical record document image and a second medical record document image, the first medical record document image and the second medical record document image are both medical record documents in image format, the first medical record document image only contains text information, and the second medical record document image contains both text information and table information.

[0037] Step 202: Use the PaddleOCR algorithm to extract text information from the first medical record document image as first identification information.

[0038] Step 203: Use the OpenCV algorithm to divide the text area and the table area in the second medical record document image.

[0039] Step 204: Use a line segment detection algorithm to enhance the table area to obtain an enhanced table area.

[0040] Step 205: Use the PaddleOCR algorithm to extract text information in the text area, and use the PP-Structure algorithm to extract text information in the enhanced table area to obtain second recognition information.

[0041] Step 206, obtaining medical image data corresponding to the orthopedic clinical medical record document.

[0042] Step 207: De-identify the first identification information, the second identification information, and the medical image data.

[0043] Step 208: Generate an orthopedic electronic health record based on the de-identified first identification information, the second identification information, and the medical image data.

[0044] In the above steps 201 to 208 of the present application, first, patient examinations, diagnoses, treatment records, medical images, and clinical notes are extracted from the documents and personal identification information is removed. Then, an automatic OCR auxiliary tool is used for text recognition and data structuring, and researchers can use the tool to generate self-collected EHR datasets for medical practice.

[0045] In this embodiment, medical imaging data refers to some CT images taken by orthopedics, and the data only needs to be de-identified. Medical medical record documents refer to clinical medical records obtained from the hospital. The records are all saved in the form of pictures. It is necessary to distinguish the table information or text information in the pictures and then identify them separately.

[0046] In another exemplary embodiment, in the above step 201, orthopedic clinical medical records are collected from the hospital.

[0047] In another exemplary embodiment, in the above steps 202-205, an automatic OCR auxiliary tool is used to identify and extract information from the document. Five types of information are extracted: patient examination records, diagnosis records, treatment records, clinical notes and medical images, specifically including:

[0048] First, use OpenCV to identify the region of interest in the document, and use PP-Structure for preliminary recognition to extract five types of information: patient examination, diagnosis, treatment, clinical notes and medical images, and then process each type of information separately. The specific implementation process is: for documents in image format, use PaddleOCR to extract the text information and obtain the first recognition information. For documents containing both text and table information, use OpenCV to segment them into different areas containing data in various formats, and then use PaddleOCR for text recognition. For the table information, a Line Segment Detector (LSD) algorithm is applied to enhance the recognition effect. After enhancement, use PP-Structure to extract the table information and obtain the second recognition information.

[0049] When using the automatic OCR auxiliary tool, the present application embodiment observed that using different colors for line enhancement would result in different OCR recognition performance. Therefore, this experiment was conducted to investigate the impact of different color line enhancement on OCR recognition. The recognition performance of 10 significantly different colors was tested on a document to evaluate their effectiveness in capturing document information.

[0050] The formula for calculating the unit-level recognition accuracy of a document is:

[0051]

[0052] Among them, Cell-level Accuracy is the cell-level recognition accuracy, δ is the number of cells in the recognition results that completely match the true values ​​of each cell in the third type of document, and ε is the total number of cells in the third type of document. In order to ensure the accuracy of the true values ​​used for experimental comparison, these true values ​​are manually identified and collected from the original documents through a manual process.

[0053] It can be seen that in the test results of 10 colors (see Table 1), the pink enhancement effect with RGB value (255, 125, 255) is the best, while the purple enhancement effect with RGB value (255, 0, 255) is the worst.

[0054] Table 1 The influence of different colors on OCR recognition effect

[0055] color RGB Cell level accuracy Pink (255，125，255) 0.96 yellow (255，255，0) 0.90 Orange (255，165，0) 0.90 Teal (0，255，255) 0.89 grey (128，128，128) 0.81 green (0，255，0) 0.78 blue (0，0，255) 0.76 red (255，0，0) 0.75 black (0，0，0) 0.73 Purple (255，0，255) 0.62

[0056] According to the experimental results on the influence of color on OCR recognition performance, the embodiment of the present application selects the best pink color for the automatic OCR auxiliary tool.

[0057] In order to illustrate the effect of the above method, a total of 30 documents were sampled in the embodiment of the present application, and 10 documents were taken from treatment records, patient examination records and diagnosis records for OCR recognition. The recognition results before and after enhancement were compared and analyzed, and a comprehensive evaluation was performed. The evaluation index uses cell-level accuracy. Among them, the true value is obtained by manual sampling and comparison.

[0058] It was observed that the enhanced cell-level recognition accuracy of treatment records, patient examination records, and diagnosis records increased by 7%, 34%, and 18%, respectively (see Table 2). This shows that the OCR recognition performance is significantly improved when enhanced using the LSD algorithm. This directly demonstrates the superiority of OCR-assisted tools over current unenhanced recognition tools.

[0059] Table 2. Cell-level recognition accuracy of treatment records, patient examination records, and diagnosis records after enhancement

[0060] Image Type Accuracy before enhancement Accuracy after enhancement Treatment Records 0.96 0.89 Patient examination records 0.88 0.54 Diagnostic Records 0.87 0.69

[0061] By using automatic OCR auxiliary tools, compare the effects before and after use, such as Figure 4 and Figure 5 As shown, Figure 4 (a) is the second medical record document image before enhancement. Figure 4 (b) is for Figure 4 The recognition result obtained by performing recognition on (a) in the figure is: Figure 5 (a) in the figure is the enhanced second medical record document image. Figure 5 (b) is for Figure 5 The recognition result obtained by performing recognition in (a) of the present invention can extract key information for constructing an electronic health record (EHR) data set with high quality. The tool can significantly reduce the manual work required for data extraction.

[0062] Secondly, the format of the extracted information is standardized using regular expressions, and Pandas is used for data organization. In order to distinguish the OCR recognition results, a keyword-based method is used to classify the extracted information by identifying whether the information contains predefined keywords related to each type of data.

[0063] For example, when keywords such as "item category" and "result" are included in information first recognized in PP-Structure, the information is classified as a patient examination.

[0064] In addition, considering the specific properties of the data: (1) the size of each image is fixed, and (2) the locations to be recognized are consistent, the embodiments of the present application extract images from predefined areas for recognition. However, during the recognition process, it can be observed that the lines separating tables in the document are usually very blurred. In order to solve this problem, the LSD algorithm is applied to automatically detect straight lines and enhance the table by making the boundaries clearer. During the table recognition process, the original data without enhanced lines often misidentifies the format information of certain areas, while after enhancement, this information is correctly recognized. This result shows that OCR auxiliary tools can effectively enhance the recognition of table information.

[0065] In an exemplary embodiment, in order to ensure the accuracy of the data generated for orthopedic electronic health records, all data information extracted from the automatic OCR auxiliary tool is manually compared and cleaned to ensure that the extracted electronic health record (EHR) is consistent with the original data, specifically including:

[0066] Medical imaging data was collected from the hospital's patient records to construct an orthopedic electronic health record (OEHR). Medical image annotations in the OEHR were completed by orthopedic experts. To further improve data quality, this application manually compared and cleaned all data extracted from the automatic OCR-assisted tools to ensure that the extracted electronic health record (EHR) was consistent with the original data. This meticulous approach helps reduce potential inaccuracies that may be caused by automatic OCR-assisted tools and ensures the high quality and reliability of the OEHR.

[0067] In an exemplary embodiment, the above step 207 mainly performs de-identification processing on sensitive data representing the patient's identity, date and patient ID, based on which the present application embodiment strictly complies with the Health Insurance Portability and Accountability Act. A series of pre-processing is performed to protect data privacy.

[0068] First, necessary key information is extracted from electronic health records (EHRs) and sensitive data containing personally identifiable information (such as name, address, and contact information) is filtered out. Patient IDs and admission IDs obtained from the documents are de-identified so that these IDs are no longer associated with specific individuals in the dataset by removing associated information.

[0069] Then, for the date information, we used a time randomization algorithm to de-identify it.

[0070] Specifically, the random time drift algorithm (RandomTime Drift), see Table 3, is used to encrypt the date information in the electronic health record (EHR), that is, the algorithm receives a date information θ and outputs the encrypted date information γ. The specific process is as follows: First, we convert the input date information θ into a string form; then, the one-way encryption algorithm SHA256 is applied to the string θ. The specific operation is to use the SHA256 hash function to generate a hash value string α, and then randomly sample α. The specific sampling process is as follows: randomly take 3 numbers from α and add them to convert it to year year; randomly take 1 number from α and convert it to month month; randomly take 2 numbers from α and add them to convert it to date day; then, year, month, and day are respectively processed by modulo addition operations to strictly follow the year, month, and day mapping in the real world to ensure the authenticity of the date information. In addition, we only encrypt the starting date information in the module containing date information, so that the date displayed in the EHR is processed to ensure consistency with the virtual time point while maintaining the interval between related dates.

[0071] Table 3 Virtual date generation process

[0072]

[0073] Then, in order to manage patient identification in the study, the embodiment of the present application uses a randomization method to assign a virtual ID to each patient and establish a mapping relationship between the actual patient ID and the virtual ID. This method ensures that the patient identity is de-identified by using the virtual ID when the data set is released.

[0074] Based on the same inventive concept, the embodiment of the present application also provides a device for generating orthopedic electronic health records based on orthopedic clinical medical record documents for realizing the method for generating orthopedic electronic health records based on orthopedic clinical medical record documents involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more devices for generating orthopedic electronic health records based on orthopedic clinical medical record documents provided below can refer to the limitations of the method for generating orthopedic electronic health records based on orthopedic clinical medical record documents above, and will not be repeated here.

[0075] In an exemplary embodiment, a device for generating an orthopedic electronic health record based on an orthopedic clinical medical record document is provided, comprising:

[0076] A medical record document acquisition module is used to acquire orthopedic clinical medical record documents; the orthopedic clinical medical record documents include: a first medical record document image and a second medical record document image, the first medical record document image and the second medical record document image are both medical record documents in image format, the first medical record document image only contains text information, and the second medical record document image contains both text information and table information.

[0077] The first text information extraction module is used to extract text information from the first medical record document image using the PaddleOCR algorithm as the first recognition information.

[0078] The area division module is used to divide the text area and table area in the second medical record document image by using the OpenCV algorithm.

[0079] The table enhancement module is used to enhance the table area using a line segment detection algorithm to obtain an enhanced table area.

[0080] The second text information extraction module is used to extract text information in the text area using the PaddleOCR algorithm, and to extract text information in the enhanced table area using the PP-Structure algorithm to obtain second recognition information.

[0081] The medical image data acquisition module is used to acquire the medical image data corresponding to the orthopedic clinical medical record document.

[0082] The de-identification module is used to perform de-identification processing on the first identification information, the second identification information and the medical image data.

[0083] The orthopedic electronic health record generation module is used to generate an orthopedic electronic health record based on the de-identified first identification information, the second identification information and the medical imaging data.

[0084] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data determined by complementarity. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for generating an orthopedic electronic health record based on an orthopedic clinical medical record document is implemented.

[0085] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0086] In an exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps in the above-mentioned method embodiment of generating orthopedic electronic health records based on orthopedic clinical medical record documents are implemented.

[0087] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, etc., but is not limited thereto.

[0088] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0089] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for generating orthopedic electronic health records based on orthopedic clinical medical records, characterized in that: include: Obtain orthopedic clinical medical records documentation; The orthopedic clinical medical record document comprises: a first medical record document image and a second medical record document image, wherein the first medical record document image and the second medical record document image are both medical record documents in image format, the first medical record document image only contains text information, and the second medical record document image contains both text information and table information; Use the PaddleOCR algorithm to extract text information from the first medical record document image as the first recognition information; Use OpenCV algorithm to divide the text area and table area in the second medical record document image; Use a line segment detection algorithm to enhance the table area to obtain an enhanced table area; Use the PaddleOCR algorithm to extract text information in the text area, and use the PP-Structure algorithm to extract text information in the enhanced table area to obtain second recognition information; Acquiring medical imaging data corresponding to the orthopedic clinical medical record document; De-identifying the first identification information, the second identification information, and the medical image data; An orthopedic electronic health record is generated based on the de-identified first identification information, the second identification information and the medical imaging data.

2. The method for generating orthopedic electronic health records based on orthopedic clinical medical records according to claim 1, characterized in that: Use the line segment detection algorithm to enhance the table area to obtain the enhanced table area, including: Determine the color with the highest unit-level recognition accuracy in the color set as the target enhancement color; A line segment detection algorithm is used to detect a table border in the table area, and the color of the table border is set to the target enhanced color to obtain an enhanced table area.

3. The method for generating orthopedic electronic health records based on orthopedic clinical medical records according to claim 2, characterized in that: Determine the color with the highest unit-level recognition accuracy in the color set as the target enhancement color, including: Select any document containing only table information as the third type of document; respectively setting the color of the table border in the third type of document to each color in the color set to obtain a plurality of enhanced documents; Based on the PaddleOCR algorithm, each enhanced document is recognized and the recognition result of each enhanced document is obtained; According to the recognition result of each enhanced document, the unit-level recognition accuracy of each enhanced document is calculated; The color corresponding to the enhanced document with the highest unit-level recognition accuracy is determined as the target enhanced color.

4. The method for generating orthopedic electronic health records based on orthopedic clinical medical records according to claim 3, characterized in that: The calculation formula for unit-level recognition accuracy is: Among them, Cell-levelAccuracy is the cell-level recognition accuracy, δ is the number of cells in the recognition result that completely match the true values ​​of each cell in the third type of document, and ε is the total number of cells in the third type of document.

5. The method for generating orthopedic electronic health records based on orthopedic clinical medical records according to claim 1, characterized in that: The first identification information, the second identification information and the medical image data are de-identified, and the process also includes: Standardizing the formats of the first identification information and the second identification information by using a regular expression to obtain standardized first identification information and standardized second identification information; Use the Pandas tool to sort the standardized first identification information and the standardized second identification information to obtain the sorted first identification information and the sorted second identification information; The keyword-based classification method is used to determine the types of the sorted first identification information and the sorted second identification information.

6. The method for generating orthopedic electronic health records based on orthopedic clinical medical records according to claim 1, characterized in that: The first identification information, the second identification information and the medical image data are de-identified, specifically including: Filter out sensitive data in the first identification information, the second identification information, and the medical image data; the sensitive data includes name, address, and contact information; Using a time randomization algorithm to replace the actual dates in the first identification information, the second identification information and the medical image data with virtual dates generated based on the time randomization algorithm; The actual IDs in the first identification information, the second identification information and the medical image data are replaced with virtual IDs generated by a randomization method.

7. The method for generating orthopedic electronic health records based on orthopedic clinical medical records according to claim 6, characterized in that: The time randomization algorithm specifically includes: Convert said actual date to a string; The character string is encrypted using the SHA256 hash function to obtain a hash value character string; Randomly select three numbers from the hash value string and add them together, and use the result of the addition as the first number D1; A number is randomly selected from the hash value string as the second number D2; Randomly select two numbers from the hash value string and add them together, and use the result of the addition as the third number D3; Calculate the year in the virtual date using the following formula according to the first number; year=3000+D1%1000+1; Calculate the month of the virtual date using the following formula according to the second number; month=D2%12+1; According to the third digit and the year and month in the virtual date, the day in the virtual date is calculated using the following formula; 8. The method for generating orthopedic electronic health records based on orthopedic clinical medical records according to claim 7, characterized in that: The leap year condition is: year%4=0 and year%100!=0, or year%400=0.

9. A device for generating orthopedic electronic health records based on orthopedic clinical medical records, characterized in that: The device for generating orthopedic electronic health records based on orthopedic clinical medical records documents applies the method for generating orthopedic electronic health records based on orthopedic clinical medical records documents according to any one of claims 1 to 8, and the device for generating orthopedic electronic health records based on orthopedic clinical medical records documents comprises: A medical record document acquisition module, used to acquire orthopedic clinical medical record documents; the orthopedic clinical medical record documents include: a first medical record document image and a second medical record document image, the first medical record document image and the second medical record document image are both medical record documents in image format, the first medical record document image only contains text information, and the second medical record document image contains both text information and table information; A first text information extraction module, used to extract text information from the first medical record document image using a PaddleOCR algorithm as first identification information; A region division module, used for dividing the text region and the table region in the second medical record document image by using an OpenCV algorithm; A table enhancement module is used to enhance the table area using a line segment detection algorithm to obtain an enhanced table area; A second text information extraction module is used to extract text information in the text area using the PaddleOCR algorithm, and to extract text information in the enhanced table area using the PP-Structure algorithm to obtain second recognition information; A medical image data acquisition module, used to acquire medical image data corresponding to the orthopedic clinical medical record document; A de-identification module, used for de-identifying the first identification information, the second identification information and the medical image data; The orthopedic electronic health record generation module is used to generate an orthopedic electronic health record based on the de-identified first identification information, the second identification information and the medical imaging data.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for generating an orthopedic electronic health record based on an orthopedic clinical medical record document as described in any one of claims 1 to 8.

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