Physical examination report interpretation method and device based on large model, electronic equipment, readable storage medium and computer program product

Abstract

The invention provides a physical examination report interpretation method and device based on a large model, electronic equipment, a readable storage medium and a computer program product, and relates to the field of artificial intelligence, in particular to the field of intelligent medical treatment. According to the implementation scheme, text recognition is carried out on a physical examination report to obtain physical examination report data; performing semantic analysis on the physical examination report data to obtain physical examination report medical data; identifying physical examination index information in the physical examination report medical data based on a pre-trained physical examination index identification model; and inputting the physical examination index information into a pre-trained physical examination report interpretation model to obtain user health analysis information.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, in particular to the field of smart medical care, and specifically to a method, device, electronic device, computer-readable storage medium and computer program product for interpreting physical examination reports based on a large model. Background Art

[0002] With the continuous advancement of medical technology and the enhancement of people's health awareness, physical examinations have become an indispensable part of people's daily lives. As an important result of physical examinations, physical examination reports contain rich health information, which is of great significance for the early detection, diagnosis and treatment of diseases. By training on massive text data, large language models can learn rich language knowledge and reasoning capabilities, thereby realizing the understanding and generation of complex texts. Applying large models pre-trained with medical corpus to the intelligent interpretation of physical examination reports can effectively reduce the workload of doctors and improve the accuracy and efficiency of interpretation, which has important practical significance and application value. Summary of the invention

[0003] The present disclosure provides a method, device, electronic device, computer-readable storage medium and computer program product for interpreting a physical examination report based on a large model.

[0004] According to one aspect of the present disclosure, a method for interpreting a physical examination report based on a large model is provided, including: performing text recognition on a physical examination report to obtain physical examination report data; performing semantic analysis on the physical examination report data to obtain physical examination report medical data; identifying physical examination indicator information in the physical examination report medical data based on a pre-trained physical examination indicator recognition model; and inputting the physical examination indicator information into a pre-trained physical examination report interpretation model to obtain user health analysis information.

[0005] According to a second aspect of the present disclosure, a physical examination report interpretation device based on a large model is provided, comprising: a data acquisition unit, configured to perform text recognition on a physical examination report to obtain physical examination report data; a data analysis unit, configured to perform semantic analysis on the physical examination report data to obtain physical examination report medical data; an indicator extraction unit, configured to identify physical examination indicator information in the physical examination report medical data based on a pre-trained physical examination indicator recognition model; and a data interpretation unit, configured to input the physical examination indicator information into a pre-trained physical examination report interpretation model to obtain user health analysis information.

[0006] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned physical examination report interpretation method.

[0007] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the processor implements the method for interpreting a physical examination report as described above.

[0008] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the above-mentioned physical examination report interpretation method when executed by a processor.

[0009] According to one or more embodiments of the present disclosure, a physical examination index recognition model is trained using medical health corpus, and combined with a pre-trained physical examination report interpretation model, an efficient automated physical examination report interpretation method and device are provided.

[0010] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0012] Figure 1 A schematic diagram showing an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0013] Figure 2 A flowchart of a method for interpreting a physical examination report based on a large model according to an embodiment of the present disclosure is shown;

[0014] Figure 3 A flowchart of a large-model-based physical examination report interpretation model training according to an embodiment of the present disclosure is shown;

[0015] Figure 4 A block diagram of a medical examination report interpretation device based on a large model according to an embodiment of the present disclosure is shown;

[0016] Figure 5 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0017] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0018] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0019] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0020] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0021] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

[0022] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the physical examination report interpretation method to be performed.

[0023] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0024] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0025] The user may use client devices 101, 102, 103, 104, 105 and / or 106 to interpret the medical examination report. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure may support any number of client devices.

[0026] Client devices 101, 102, 103, 104, 105 and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, game systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smart phones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Game systems may include various handheld game devices, Internet-enabled game devices, etc. Client devices are capable of executing various different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and may use various communication protocols.

[0027] The network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0028] Server 120 may include one or more general purpose computers, dedicated server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0029] The computing units in the server 120 may run one or more operating systems including any of the above operating systems and any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0030] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and / or 106. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and / or 106.

[0031] In some embodiments, the server 120 may be a server of a distributed system, or a server combined with a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in a cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0032] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The databases 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0033] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0034] Figure 1 The system 100 may be configured and operated in various ways to enable the application of various methods and apparatuses described in the present disclosure.

[0035] Figure 2 is a flow chart showing a method 200 for interpreting a physical examination report based on a large model according to an embodiment of the present disclosure, such as Figure 2 As shown, the physical examination report interpretation method may include: step S202, performing text recognition on the physical examination report to obtain physical examination report data; step S204, performing semantic analysis on the physical examination report data to obtain physical examination report medical data; step S206, identifying physical examination indicator information in the physical examination report medical data based on a pre-trained physical examination indicator recognition model; and step S208, inputting the physical examination indicator information into a pre-trained physical examination report interpretation model to obtain user health analysis information.

[0036] The physical examination report interpretation method according to the embodiment of the present disclosure can respond to the user's interpretation request and can achieve efficient and accurate automatic interpretation of the physical examination report.

[0037] Here, it should be noted that the physical examination report includes at least one of a plain text report, a PDF report, and a picture report. In some embodiments, the picture report can be converted into a plain text report by optical recognition technology. The PDF report is converted into a picture report by a built-in component, and the converted picture report is then converted into a plain text report by optical recognition technology.

[0038] In some embodiments, after step S202, the obtained physical examination report data is also preprocessed to remove irrelevant information, standardize the format, and process special characters to improve the purity and consistency of the physical examination report data.

[0039] In some embodiments, in step S204, semantic analysis of the physical examination report data includes at least one of the following: layout analysis, title level recognition, and table recognition.

[0040] In some embodiments, layout analysis includes at least one of the following: document segmentation, region classification, row and column extraction, and character extraction.

[0041] In this embodiment, layout analysis is aimed at texts such as medical examination reports. It analyzes and understands the structure of the text, breaks down the content layout through document segmentation, area classification, line and character extraction, identifies different areas and the content within the area, and can accurately extract physical examination indicator information.

[0042] In some embodiments, title level identification includes identifying department information.

[0043] In this embodiment, department information is reflected at the title level, so accurately extracting the hierarchical structure content in the physical examination report will help improve the accuracy of the physical examination report in the future. The accuracy of title recognition can be improved through text area recognition, feature extraction, hierarchical determination, building a hierarchical tree structure and post-processing. The extraction of department information can improve the pertinence of the interpretation of the physical examination report.

[0044] In some embodiments, table recognition includes at least one of the following: table area detection, row and column segmentation, cell segmentation and extraction, and table structure analysis.

[0045] In this embodiment, the common tables in the physical examination report are used to display the examination results. Accurate table recognition is particularly important for extracting abnormal indicators. The present invention improves the accuracy of the indicator extraction results by means of table area detection, row and column segmentation, cell segmentation and extraction, and table structure analysis.

[0046] Exemplarily, the physical examination report data structure after semantic analysis of the physical examination report data includes basic user information and indicator information. The basic user information includes at least the name of the patient / patient, the gender of the patient / patient, the age of the patient / patient, and the physiological period of the patient / patient. The indicator information includes at least the name of the indicator / part, the indicator value, the unit of the indicator value, the name of the report, the source of the sample, the normal reference range of the indicator value, and the abnormal description of the indicator or part.

[0047] It should be noted that the above examples are not intended to limit the structure of the physical examination report data in the present invention, and the structure of the physical examination report data may be adjusted accordingly according to the actual application scenario.

[0048] In some embodiments, in step S206, the pre-trained physical examination index recognition model is based on a pre-trained large language model and is trained using medical health corpus as training samples.

[0049] In some embodiments, the medical health corpus includes at least one of clinical desensitization data, medical knowledge graphs, and multimodal imaging data.

[0050] It should be noted that, in the technical solution of this disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good customs. Clinical medical data will involve patient privacy and medical ethics. In view of the data privacy faced by medical data in the era of network sharing, the boundaries between data ownership and use rights are unclear. In the technical solution of this disclosure, the clinical data used is desensitized data after removing the patient's name, contact information (telephone, email), ID card information, address, institution name, and doctor information.

[0051] In some embodiments, the medical knowledge graph consists of nodes and edges. Nodes represent entities, concepts, or attribute values. Any object, place, or person can be a node. Edges represent the attributes of entities or the relationship between entities. Triples are the basic representation of knowledge graphs. For example, a node can be a department or a disease, such as hypertension. Edges describe the "disease-department" relationship between hypertension and cardiology. (hypertension, department, cardiology) is a simple triple.

[0052] In some embodiments, the multimodal image data includes at least one of computed tomography data, magnetic resonance imaging data, and positron emission tomography data. Different image data can provide different information, which can be combined to provide a more comprehensive understanding of the user's health status, thereby providing a more accurate physical examination report.

[0053] In some embodiments, Figure 3 is a flowchart showing the training of a physical examination report interpretation model according to an embodiment of the present disclosure, such as Figure 3As shown, the process 300 of training the physical examination report interpretation model includes: step S302, obtaining physical examination report sample data, wherein the physical examination report sample data includes at least one of user-authorized data and physical examination report data collected from a physical examination institution; step S304 obtaining a target label, wherein the target label includes preset interpretation information of the physical examination report sample data based on the physical examination report sample data; and step S306, training a large model based on the physical examination report sample data and the target label to obtain the physical examination report interpretation model.

[0054] Based on the pre-trained physical examination indicator recognition model and rich training samples, a physical examination report interpretation model based on a large model is established, which can understand the complex physical examination report text and generate physical examination report interpretation information, effectively reducing the workload of doctors, thereby improving the accuracy and efficiency of physical examination report interpretation.

[0055] In some embodiments, in step S302, after obtaining the user's authorized data and the physical examination report data collected from the physical examination institution, sensitive information including the patient's name, contact information (telephone, email), ID card information, address, institution name, and doctor information is removed.

[0056] In some embodiments, in step S304, the interpretation information of the sample data of the physical examination report is to hire a doctor to write an interpretation text for the obtained physical examination report, ensuring that each physical examination report is accompanied by the interpretation information of a professional physician. These interpretation information include an explanation of each physical examination indicator, potential health problems determined by analyzing the indicators, and further examinations and precautions given. It also includes interpretations of different pathological conditions based on the physical examination report data. After the doctor gives the interpretation information of the physical examination report, it also includes hiring a medical expert to conduct a consistency check on the results of the interpretation information to ensure the scientificity and accuracy of the content of the interpretation information.

[0057] In some embodiments, the information in the physical examination report includes at least basic information, physical examination indicator information and doctor's advice. There are complex logical relationships between the various parts. For this reason, the physical examination report interpretation model adopts an attention mechanism and a logic-based context awareness mechanism, and uses built-in rules to capture some semantic features and semantic relationships between phrases in the same sentence in the physical examination report, thereby improving the physical examination report interpretation model's ability to understand long texts.

[0058] In some embodiments, in step S306, training a large model based on the sample data of the physical examination report and the target label to obtain the physical examination report interpretation model includes: inputting the sample data of the physical examination report into the large model to obtain the prediction result output by the large model, and then fine-tuning the parameters of the large model by comparing the prediction result output by the large model with the target label, repeating the above operation until the loss value of the large model is less than the preset setting value, and the training of the physical examination report interpretation model is completed. The setting value is set according to the actual scenario and will not be repeated here.

[0059] In this embodiment, the context-aware mechanism automatically considers the global and local context information of the input medical examination report during the interpretation process, thereby generating a more accurate and natural interpretation.

[0060] In some embodiments, in step S208, the user health analysis information includes but is not limited to the report name, abnormal item interpretation, overall conclusion, recommended examination, recommended test, health advice, recommended department for consultation, and reference documents. Among them, health advice includes but is not limited to diet, nutrition, exercise, work and rest, and medicine.

[0061] In some embodiments, after step S208, the key indicators and potential problems in the physical examination report are displayed to the user in the form of charts and lists to ensure that the information is clear and intuitive. In addition, the system also provides a detailed interpretation report to explain the meaning of each indicator and possible health risks for the user's reference.

[0062] Figure 4 4 is a block diagram showing a physical examination report interpretation device 400 according to an embodiment of the present disclosure. Figure 4 , the physical examination report interpretation device 400 is described in detail.

[0063] In some embodiments, Figure 4 As shown, the physical examination report interpretation device 400 includes a data acquisition unit 402, a data analysis unit 404, an indicator extraction unit 406 and a data interpretation unit 408. The data acquisition unit 402 is configured to perform text recognition on the physical examination report to obtain physical examination report data. The data analysis unit 404 is configured to perform semantic analysis on the physical examination report data to obtain physical examination report medical data. The indicator extraction unit 406 is configured to identify the physical examination indicator information in the physical examination report medical data based on a pre-trained physical examination indicator recognition model. The data interpretation unit 408 is configured to input the physical examination indicator information into a pre-trained physical examination report interpretation model to obtain user health analysis information.

[0064] In some embodiments, the data acquisition unit 402 is further configured to: process the possible PDF report and the picture report as follows: convert the picture report into a plain text report by optical recognition technology; convert the PDF report into a picture report by a built-in component, and then convert the converted picture report into a plain text report by optical recognition technology.

[0065] In some embodiments, the data analysis unit 404 is further configured to: perform semantic analysis on the physical examination report data including at least one of the following: layout analysis, title level recognition, and table recognition.

[0066] In some embodiments, the data analysis unit 404 is further configured to analyze the layout including at least one of the following: document segmentation, region classification, row and column extraction, and character extraction.

[0067] In some embodiments, the data analysis unit 404 is further configured to: identify the title level including identifying department information.

[0068] In some embodiments, the data analysis unit 404 is further configured to: identify the table including at least one of the following: table area detection, row and column segmentation, cell segmentation and extraction, and table structure analysis.

[0069] In some embodiments, the index extraction unit 406 is further configured as follows: the pre-trained physical examination index recognition model is obtained by training based on a pre-trained large language model using medical health corpus as training samples. The medical health corpus includes at least one of clinical desensitization data, medical knowledge graphs, and multimodal imaging data.

[0070] In some embodiments, the data interpretation unit 408 is further configured to: present key indicators and potential problems in the physical examination report to the user in the form of charts and lists to ensure that the information is clear and intuitive. In addition, the system also provides a detailed interpretation report to explain the meaning of each indicator and possible health risks for the user's reference.

[0071] According to an embodiment of the present disclosure, an electronic device, a readable storage medium and a computer program product are also provided.

[0072] refer to Figure 5, a block diagram of an electronic device 500 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0073] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0074] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device that can input information to the electronic device 500. The input unit 506 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 507 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 can include but is not limited to a disk, an optical disk. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0075] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method 200 in any other appropriate manner (e.g., by means of firmware).

[0076] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0077] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0078] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0079] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0080] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0081] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0082] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0083] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A method for interpreting a physical examination report based on a large model, comprising: Perform text recognition on the physical examination report to obtain the physical examination report data; Performing semantic analysis on the physical examination report data to obtain medical data of the physical examination report; Identify the physical examination index information in the medical data of the physical examination report based on the pre-trained physical examination index recognition model; as well as The physical examination index information is input into a pre-trained physical examination report interpretation model to obtain user health analysis information.

2. The method for interpreting a physical examination report based on a large model according to claim 1, wherein: The semantic analysis of the physical examination report data includes at least one of the following: layout analysis, title level recognition, and table recognition.

3. The method for interpreting a physical examination report based on a large model according to claim 2, wherein: The layout analysis includes at least one of the following: document segmentation, region classification, row and column extraction, and character extraction.

4. The method for interpreting a physical examination report based on a large model according to claim 2, wherein: The title level identification includes identifying department information.

5. The method for interpreting a physical examination report based on a large model according to claim 2, wherein: The table recognition includes at least one of the following: table area detection, row and column segmentation, cell segmentation and extraction, and table structure analysis.

6. The method for interpreting a physical examination report based on a large model according to claim 1, wherein: The pre-trained physical examination index recognition model is based on a pre-trained large language model and is trained using medical and health corpus as training samples.

7. The method for interpreting a physical examination report based on a large model according to claim 6, wherein: The medical health corpus includes at least one of clinical anesthetized data, medical knowledge graphs, and multimodal imaging data.

8. The method for interpreting a physical examination report based on a large model according to claim 1, wherein: The pre-training process of the physical examination report interpretation model includes: Acquire sample data of a physical examination report, wherein the sample data of the physical examination report includes at least one of data authorized by a user and physical examination report data collected from a physical examination institution; Acquire a target tag, wherein the target tag includes preset interpretation information of the sample data of the physical examination report based on the sample data of the physical examination report; and A large model is trained based on the physical examination report sample data and the target label to obtain the physical examination report interpretation model.

9. A physical examination report interpretation device based on a large model, comprising: A data acquisition unit is configured to perform text recognition on the physical examination report to obtain physical examination report data; A data analysis unit, configured to perform semantic analysis on the physical examination report data to obtain medical data of the physical examination report; An indicator extraction unit, configured to identify the physical examination indicator information in the medical data of the physical examination report based on a pre-trained physical examination indicator recognition model; as well as The data interpretation unit is configured to input the physical examination index information into a pre-trained physical examination report interpretation model to obtain user health analysis information.

10. The medical examination report interpretation device based on a large model according to claim 9, wherein: The semantic analysis of the physical examination report data includes at least one of the following: layout analysis, title level recognition, and table recognition.

11. The medical examination report interpretation device based on a large model according to claim 10, wherein: The layout analysis includes at least one of the following: document segmentation, region classification, row and column extraction, and character extraction.

12. The medical examination report interpretation device based on a large model according to claim 10, wherein: The title level identification includes identifying department information.

13. The medical examination report interpretation device based on a large model according to claim 10, wherein: The table recognition includes at least one of the following: table area detection, row and column segmentation, cell segmentation and extraction, and table structure analysis.

14. The medical examination report interpretation device based on a large model according to claim 9, wherein: The pre-trained physical examination index recognition model is based on a pre-trained large language model and is trained using medical and health corpus as training samples.

15. The medical examination report interpretation device based on a large model according to claim 14, wherein: The medical health corpus includes at least one of clinical anesthetized data, medical knowledge graphs, and multimodal imaging data.

16. The medical examination report interpretation device based on a large model according to claim 9, wherein: The pre-training process of the physical examination report interpretation model includes: Acquire sample data of a physical examination report, wherein the sample data of the physical examination report includes at least one of data authorized by a user and physical examination report data collected from a physical examination institution; Acquire a target tag, wherein the target tag includes preset interpretation information of the sample data of the physical examination report based on the sample data of the physical examination report; and A large model is trained based on the physical examination report sample data and the target label to obtain the physical examination report interpretation model.

17. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-8.

19. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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