Pre-inquiry information generation method and device, equipment and storage medium

Abstract

The invention provides a pre-inquiry information generation method and device, equipment and a storage medium, and relates to the technical field of medical information processing. The pre-inquiry information generation method comprises the following steps: acquiring treatment information of a patient; based on the doctor seeing information, acquiring an inquiry record and symptom information of the patient through a preset inquiry model; based on the symptom information, generating a complete symptom set of the patient through a preset dialectical model; and combining the inquiry record with the complete symptom set to form pre-inquiry information. According to the embodiment of the invention, a doctor can be simulated to carry out active guide type inquiry, and the inquiry accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of medical information processing technology, and in particular to a method, device, equipment and storage medium for generating pre-diagnosis information. Background Art

[0002] In the traditional Chinese medicine diagnosis and treatment process, medical interview is a method of diagnosing the disease by asking the patient or the patient's companion purposeful questions to understand the occurrence and development of the disease, the diagnosis and treatment process, the current symptoms and all other conditions related to the disease.

[0003] At present, the general consultation process is completed by the doctor himself or replaced by filling out forms, which is inefficient. How to combine TCM consultation with computer artificial intelligence technology to accurately collect patient symptom information before the doctor's consultation, improve consultation efficiency and the accuracy of the doctor's diagnosis, is an urgent problem to be solved in this field. Summary of the invention

[0004] According to one aspect of the present application, a method for generating pre-consultation information is provided, comprising: obtaining the patient's medical information; based on the medical information, obtaining the patient's medical consultation record and symptom information through a preset medical consultation model; based on the symptom information, generating the patient's complete symptom set through a preset dialectical model; combining the medical consultation record and the complete symptom set to form pre-consultation information.

[0005] According to some embodiments, before obtaining the patient's medical information, real doctor-patient interaction data is obtained; and the medical consultation model is trained using the doctor-patient interaction data.

[0006] According to some embodiments, the medical consultation model is trained using doctor-patient interaction data, including: generating question-and-answer reference data based on the doctor-patient interaction data; revising the question-and-answer reference data; and fine-tuning the medical consultation model using the revised question-and-answer reference data.

[0007] According to some embodiments, based on the medical information, the patient's medical consultation record and symptom information are obtained through a preset medical consultation model, including: according to the medical information, guiding the medical consultation model to execute the medical consultation process through preset medical consultation prompts to obtain the patient's chief complaint information, the chief complaint information including the patient's medical history information; generating a medical consultation record based on the chief complaint information; and extracting symptom information from the chief complaint information.

[0008] According to some embodiments, extracting symptom information from the chief complaint information includes: obtaining the patient's symptom information from the chief complaint information through a medical inquiry model; converting the symptom information into an external standard symptom set; matching the external standard symptom set with a preset internal standard knowledge set to generate an internal standard symptom set.

[0009] According to some embodiments, a complete symptom set for the patient is generated through a preset dialectical model based on symptom information, including: obtaining a predicted syndrome type through a dialectical model based on an internal standard symptom set; obtaining a symptom set corresponding to the predicted syndrome type based on an internal standard knowledge set; comparing the internal standard symptom set with the symptom set corresponding to the predicted syndrome type to obtain a supplementary symptom set; pushing the supplementary symptom set to the patient, and generating a complete symptom set based on the patient's feedback.

[0010] According to some embodiments, a supplementary symptom set is pushed to the patient, and a complete symptom set is generated based on the patient's feedback, including: obtaining the symptoms selected by the patient in the supplementary symptom set; generating a complete symptom set based on the internal standard symptom set and the symptoms selected by the patient in the supplementary symptom set.

[0011] According to one aspect of the present application, a pre-consultation information generating device is provided, comprising: an information acquisition module for acquiring a patient's medical information; a first application module for acquiring the patient's medical consultation record and symptom information through a preset medical consultation model based on the medical consultation information; a second application module for generating a complete symptom set of the patient through a preset dialectical model based on the symptom information; and an information display module for combining the medical consultation record and the complete symptom set to form pre-consultation information.

[0012] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method.

[0013] According to one aspect of the present application, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the aforementioned method is implemented.

[0014] According to the embodiments of the present application, a large model trained with real data of doctor-patient interaction can be used to simulate doctors' free, multi-path, and actively guided medical consultations, so that the medical consultation information is more complete and standardized, and a more complete symptom set is obtained through the dialectical model and syndrome type knowledge set, so that the patient's symptom extraction rate is higher and more accurate.

[0015] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application.

[0017] Figure 1A schematic diagram of a pre-diagnosis system according to an exemplary embodiment of the present application is shown.

[0018] Figure 2 A flow chart showing a method for generating pre-diagnosis information according to an exemplary embodiment of the present application is shown.

[0019] Figure 3 A block diagram of a pre-diagnosis information generating device according to an exemplary embodiment of the present application is shown.

[0020] Figure 4 A block diagram of an electronic device according to an exemplary embodiment of the present application is shown. DETAILED DESCRIPTION

[0021] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0022] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0023] The described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application may be put into practice without one or more of these specific details, or other modes, components, materials, devices or operations may be adopted. In these cases, known structures, methods, devices, realizations, materials or operations will not be shown or described in detail.

[0024] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0025] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0026] The present application provides a method, device, equipment and storage medium for generating pre-diagnosis information, which can conduct active guided diagnosis through a model to improve the efficiency and accuracy of obtaining patient information and symptoms.

[0027] A method, apparatus, device and storage medium for generating pre-diagnosis information according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0028] Figure 1 A schematic diagram of a pre-diagnosis system according to an exemplary embodiment of the present application is shown.

[0029] like Figure 1 As shown, the pre-consultation system 100 includes a consultation model 110 , a dialectical model 120 and an interactive interface 130 .

[0030] Before entering the consultation process, the pre-consultation system 100 obtains the patient's input information on the interactive interface 130 and extracts the patient's consultation information therefrom.

[0031] According to some embodiments, the pre-consultation system 100 may be connected to the hospital registration system through a preset interface to obtain the patient's medical information.

[0032] The consultation model 110 enters the consultation process through preset consultation prompts (text or voice), and actively guides the patient to conduct question-and-answer interaction through the interactive interface 130 to obtain the patient's main complaint information.

[0033] According to some embodiments, the medical inquiry model 110 is a large medical model that has been trained using corpus data such as medical books and basic medical terms, and is fine-tuned using question-and-answer reference data generated based on doctor-patient interaction data.

[0034] The medical consultation model 110 summarizes and organizes the patient's chief complaint information to generate a medical consultation record and extracts the patient's medical history information.

[0035] Furthermore, the consultation model 110 extracts the patient's symptom information from the patient's chief complaint information, and converts the patient's symptom information into an external standard symptom set through standard symptom term conversion.

[0036] The pre-diagnosis system 100 matches the external standard symptom set with the preset internal standard knowledge set, thereby achieving alignment of internal and external symptoms to generate an internal standard symptom set.

[0037] The dialectical model 120 predicts the syndrome type based on the generated internal standard symptom set, recommends the syndrome type with the highest prediction probability from the prediction results, and obtains the symptom set corresponding to the predicted syndrome type through the internal standard knowledge set.

[0038] According to some embodiments, the dialectical model 120 is a model constructed based on a traditional algorithmic model or a precise knowledge set.

[0039] The pre-diagnosis system 100 compares the internal standard symptom set with the symptom set corresponding to the predicted syndrome type, removes the symptoms selected by the patient in the internal standard symptom set, and obtains the symptoms that the patient may experience as a supplementary symptom set.

[0040] The pre-consultation system 100 prompts the patient to select a supplementary symptom from the supplementary symptom set through the interactive interface 130 , and generates a complete symptom set for the patient based on the patient's feedback.

[0041] The pre-consultation system 100 combines the consultation record with the patient's complete symptom set, and pushes the formed pre-consultation information to the doctor's end for the doctor's reference during the actual diagnosis and treatment of the patient.

[0042] Figure 2 A flow chart showing a method for generating pre-diagnosis information according to an exemplary embodiment of the present application is shown.

[0043] like Figure 2 As shown, in step S100, the pre-diagnosis information generating device obtains the patient's medical information.

[0044] For example, in step S100, the pre-diagnosis information generating device obtains the patient's medical information through the information input by the patient on the system interaction interface or from the relevant system of the hospital (such as the registration system) through a preset interface.

[0045] According to some embodiments, the patient's medical information includes the medical category (such as first visit / follow-up visit) and the patient's personal information.

[0046] In step S200, based on the medical consultation information, the pre-diagnosis information generating device obtains the patient's medical consultation record and symptom information through a preset medical consultation model.

[0047] For example, in step S200, based on the patient's medical information, the pre-diagnosis information generating device obtains the patient's medical consultation record and symptom information through a trained preset diagnosis model.

[0048] Before obtaining the patient's medical information, the pre-inquiry information generating device first obtains the real doctor-patient interaction data.

[0049] According to some embodiments, the doctor-patient interaction data includes real audio recordings of communications between doctors and patients of various diseases and departments in multiple hospitals.

[0050] Furthermore, the pre-consultation information generating device processes the doctor-patient interaction data through a preset multimodal big model and generates recorded text data, and formats the recorded text data through the multimodal big model according to the preset prompts (text or voice) of the multimodal big model to obtain question and answer reference data.

[0051] The pre-questioning information generating device obtains the annotations and revision opinions of professionals with medical knowledge on the question and answer reference data, and forms a revised sample of the question and answer reference data. The pre-questioning information generating device revises the revised sample of the question and answer reference data through the question and answer model according to the sample prompts preset by the question and answer model, and obtains the revised question and answer reference data. The revised question and answer reference data can cover all relevant departments of the hospital.

[0052] Furthermore, the pre-questioning information generating device fine-tunes the question-and-answer model through the revised question-and-answer reference data, thereby optimizing some parameters of the question-and-answer model and enabling the question-and-answer model to have the ability to guide question-and-answer sessions for different types of diseases.

[0053] After obtaining the patient's medical information, the pre-inquiry information generating device guides the inquiry model to perform an inquiry process corresponding to the patient's medical information through preset inquiry prompts to collect the patient's chief complaint information.

[0054] For example, the preset consultation prompt may be "Please consult according to the patient's visiting department and consultation category". The consultation model interacts with the patient through the system interactive interface according to the consultation prompt, and obtains the chief complaint information including the patient's medical history information through active guided dialogue.

[0055] According to the patient's chief complaint information, the pre-inquiry information generating device can guide the inquiry model to summarize and organize the information according to the preset information processing prompts to obtain the inquiry record and the patient's medical history information.

[0056] Furthermore, the pre-questioning information generating device extracts the patient's symptom information from the chief complaint information through the questioning model, and converts the patient's symptom information into an external standard symptom set.

[0057] For example, the patient's chief complaint information expresses the symptom information of "I feel uncomfortable in my stomach", and the medical consultation model trained with corpus data such as medical books and basic medical terms contains the standard symptom term "abdominal discomfort" corresponding to "I feel uncomfortable in my stomach". The pre-medical consultation information generation device guides the medical consultation model to convert the natural language "I feel uncomfortable in my stomach" into "abdominal discomfort" through a preset term conversion prompt "Please convert the symptoms in the conversation into standard symptom terms", thereby obtaining the external standard symptom set corresponding to the patient's symptom information.

[0058] The pre-diagnosis information generating device matches the patient's external standard symptom set with a preset internal standard knowledge set to determine whether the external standard symptoms after the diagnosis model conversion are aligned with the internal standard symptoms in the internal standard knowledge set, and generates an internal standard symptom set based on the matching results.

[0059] According to some embodiments, the pre-diagnosis information generation device can perform similarity matching between the patient's external standard symptom set and the preset internal standard knowledge set. The pre-diagnosis information generation device uses the diagnosis model to perform similarity analysis between the terms of the external standard symptoms and the similar term set of the internal standard symptoms, and obtains the internal standard symptom terms with the highest similarity probability, which are used as the query or matching algorithm keywords for the internal standard knowledge set and the model call, to achieve alignment from the colloquial symptom terms to the internal standard symptom terms.

[0060] For example, the colloquial symptom term "I feel uncomfortable in my stomach" can be interpreted as "abdominal discomfort" through the medical consultation model. The pre-medical consultation information generation device performs similarity segmentation analysis on "abdominal discomfort" through the medical consultation model to obtain "abdomen, abdomen, discomfort", etc., and queries similar internal standard symptom terms based on the segmentation information, such as: "abdominal pain", "head discomfort", "hand discomfort", etc. The pre-medical consultation information generation device analyzes the similarity of "abdominal discomfort" with internal standard symptom terms such as "abdominal pain", "head discomfort", "hand discomfort", etc. through the medical consultation model, and outputs "abdominal pain" with the highest similarity.

[0061] Furthermore, the pre-diagnosis information generating device obtains the correspondence between the colloquial symptom term "stomach discomfort" and the internal standard symptom term "abdominal pain", and stores the correspondence. If the same type of colloquial symptom term appears again in the future, the pre-diagnosis information generating device can directly interpret the same type of colloquial symptom term as "abdominal pain" based on the correspondence stored in the database, and no longer analyze it through the diagnosis model.

[0062] In step S300, based on the symptom information, the pre-diagnosis information generating device generates a complete symptom set of the patient through a preset dialectical model.

[0063] For example, in step S300, based on the internal standard symptom set corresponding to the patient's symptom information, the pre-diagnosis information generating device predicts the syndrome type through a preset dialectical model and generates a complete symptom set.

[0064] According to some embodiments, before predicting the syndrome type, the dialectical model has been trained with the corresponding internal standard knowledge set.

[0065] The pre-diagnosis information generating device inputs the internal standard symptom set into the trained dialectical model, and predicts the syndrome type corresponding to the internal standard symptom set through the dialectical model.

[0066] For example, the internal standard symptom set corresponding to the patient includes the symptoms of "sore throat" and "headache", while in the internal standard knowledge set used to train the dialectical model, "sore throat" is associated with "cold" and "headache" is associated with "cold", so the dialectical model predicts that the patient has a higher probability of "cold" after algorithm matching. The dialectical model recommends "cold" as the predicted syndrome type to the pre-diagnosis information generation device.

[0067] The pre-diagnosis information generating device compares the internal standard symptom set with the symptom set corresponding to the predicted syndrome type to obtain a supplementary symptom set for the current patient.

[0068] For example, the "cold" predicted by the dialectical model is also associated with the symptom of "fever" in the symptoms of the internal standard knowledge set. The pre-diagnosis information generation device compares the symptom set of "sore throat", "headache" and "fever" corresponding to the predicted "cold" with the internal standard symptom set obtained through the diagnosis model, determines that the symptom of "fever" is not included in the internal standard symptom set, and uses "fever" as a supplementary symptom set for the current patient.

[0069] The pre-diagnosis information generating device pushes the internal standard symptom set and the supplementary symptom set to the patient through the system interactive interface, and prompts the patient to select the supplementary symptoms.

[0070] Furthermore, the pre-diagnosis information generating device obtains the supplementary symptoms selected by the patient through the system interactive interface, and generates a complete symptom set for the patient in combination with the internal standard symptom set.

[0071] In step S400, the pre-diagnosis information generating device combines the medical consultation record and the complete symptom set to form pre-diagnosis information.

[0072] For example, in step S400, the pre-diagnosis information generating device combines the diagnosis record obtained through the diagnosis model and the complete symptom set obtained through the dialectical model to form the pre-diagnosis information.

[0073] According to some embodiments, the pre-diagnosis information generating device pushes the pre-diagnosis information to the doctor's end for the doctor's reference during the diagnosis and treatment process.

[0074] According to the embodiments of the present application, the technical solution of the present application is not limited to a specific consultation process. The model can be used to simulate the doctor's active and guided consultation, making the consultation information more complete and standardized. The complete symptom set can be obtained through the dialectical model and related knowledge set, thereby improving the symptom extraction rate.

[0075] Figure 3 A block diagram of a pre-diagnosis information generating device according to an exemplary embodiment of the present application is shown.

[0076] like Figure 3 As shown, the pre-diagnosis information generating device 200 is applied to the pre-diagnosis system, and includes an information acquisition module 210 , a first application module 220 , a second application module 230 and an information display module 240 .

[0077] The information acquisition module 210 acquires the patient's medical information through the information input by the patient on the system interaction interface or from the relevant system of the hospital (such as the registration system) through a preset interface.

[0078] Before obtaining the patient's medical information, the first application module 220 obtains real doctor-patient interaction data.

[0079] The first application module 220 processes the doctor-patient interaction data through a preset multimodal big model and generates audio text data, and formats the audio text data through the multimodal big model according to preset prompts of the multimodal big model to obtain question and answer reference data.

[0080] The first application module 220 obtains annotations and revision opinions on the question and answer reference data from professionals with medical knowledge, and forms a revised sample of the question and answer reference data.

[0081] The first application module 220 revises the revised samples of the question and answer reference data through the question and answer model according to the sample prompts preset by the question and answer model to obtain the revised question and answer reference data.

[0082] The first application module 220 fine-tunes the question-answering model using the revised question-answering reference data.

[0083] After obtaining the patient's medical information, the first application module 220 guides the medical consultation model to perform a medical consultation process corresponding to the patient's medical information through preset medical consultation prompts to collect the patient's chief complaint information.

[0084] According to the patient's chief complaint information, the first application module 220 can guide the consultation model to summarize and organize the information according to the preset information processing prompts to obtain the consultation record and the patient's medical history information.

[0085] The first application module 220 extracts the patient's symptom information from the chief complaint information through the consultation model, and converts the patient's symptom information into an external standard symptom set.

[0086] The first application module 220 matches the patient's external standard symptom set with a preset internal standard knowledge set, and generates an internal standard symptom set according to the matching result.

[0087] The second application module 230 inputs the internal standard symptom set into the trained dialectical model, and predicts the syndrome type corresponding to the internal standard symptom set through the dialectical model.

[0088] The second application module 230 compares the internal standard symptom set with the symptom set corresponding to the predicted syndrome type to obtain a supplementary symptom set for the current patient.

[0089] The second application module 230 pushes the internal standard symptom set and the supplementary symptom set to the patient through the system interaction interface, and prompts the patient to select a supplementary symptom.

[0090] The second application module 230 obtains the supplementary symptoms selected by the patient through the system interaction interface, and generates a complete symptom set for the patient in combination with the internal standard symptom set.

[0091] The information display module 240 combines the medical consultation records obtained through the medical consultation model and the complete symptom set obtained through the dialectical model to form pre-medical consultation information.

[0092] The information display module 240 pre-consultation information generating device pushes the pre-consultation information to the doctor end.

[0093] Figure 4 A block diagram of an electronic device according to an exemplary embodiment of the present application is shown.

[0094] like Figure 4 As shown, the electronic device 600 is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0095] like Figure 4 As shown, the electronic device 600 is in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc. The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 executes the methods described in this specification according to various exemplary embodiments of the present application. For example, the processing unit 610 may execute the following: Figure 2 The method shown in .

[0096] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0097] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0098] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0099] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0100] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by combining software with necessary hardware. The technical solution according to the embodiment of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal or a network device, etc.) to execute the method according to the embodiment of the present application.

[0101] The software product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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 thereof.

[0102] Computer readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program codes contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0103] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0104] The computer-readable medium carries one or more programs. When the one or more programs are executed by a device, the computer-readable medium implements the aforementioned functions.

[0105] Those skilled in the art will appreciate that the above modules can be distributed in the device according to the description of the embodiment, or can be changed accordingly and only used in one or more devices different from the embodiment. The modules of the above embodiments can be combined into one module, or further divided into multiple sub-modules.

[0106] The embodiments of the present application are described in detail above, and the description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, changes or deformations made by those skilled in the art based on the idea of ​​the present application, the specific implementation method and the scope of application of the present application, all belong to the scope of protection of the present application. In summary, the content of this specification should not be construed as limiting the present application.

Claims

1. A method for generating pre-diagnosis information, characterized in that: include: Obtain patient medical information; Based on the medical consultation information, obtaining the patient's medical consultation record and symptom information through a preset medical consultation model; Based on the symptom information, a complete symptom set of the patient is generated through a preset dialectical model; The medical consultation record and the complete symptom set are combined to form the pre-medical consultation information.

2. The method according to claim 1, characterized in that Before obtaining the patient's medical information, the method further includes: Obtain real doctor-patient interaction data; The consultation model is trained using the doctor-patient interaction data.

3. The method according to claim 2, characterized in that The consultation model is trained using the doctor-patient interaction data, including: Generate question-and-answer reference data based on the doctor-patient interaction data; Revise the question and answer reference data; The question-answering model is fine-tuned and trained using the revised question-answering reference data.

4. The method according to claim 1, characterized in that: Based on the medical consultation information, the patient's medical consultation record and symptom information are obtained through a preset medical consultation model, including: According to the medical consultation information, guiding the medical consultation model to execute the medical consultation process through preset medical consultation prompts to obtain the patient's chief complaint information, wherein the chief complaint information includes the patient's medical history information; Generate the medical consultation record according to the chief complaint information; and The symptom information is extracted from the chief complaint information.

5. The method according to claim 4, characterized in that Extracting the symptom information from the chief complaint information includes: Acquiring the patient's symptom information from the chief complaint information through the medical inquiry model; converting the symptom information into an external standard symptom set; The external standard symptom set is matched with a preset internal standard knowledge set to generate an internal standard symptom set.

6. The method according to claim 5, characterized in that Based on the symptom information, a complete symptom set of the patient is generated through a preset dialectical model, including: Based on the internal standard symptom set, the predicted syndrome type is obtained through the dialectical model; According to the internal standard knowledge set, obtaining a symptom set corresponding to the predicted syndrome type; Comparing the internal standard symptom set with the symptom set corresponding to the predicted syndrome type to obtain a supplementary symptom set; The supplementary symptom set is pushed to the patient, and the complete symptom set is generated based on the patient's feedback.

7. The method according to claim 6, characterized in that The supplementary symptom set is pushed to the patient, and the complete symptom set is generated according to the patient's feedback, including: Obtaining the symptom selected by the patient from the supplementary symptom set; The complete symptom set is generated based on the internal standard symptom set and the symptoms selected by the patient in the supplementary symptom set.

8. A pre-diagnosis information generating device, characterized in that: include: An information acquisition module is used to obtain the patient's medical information; A first application module is used to obtain the patient's medical consultation record and symptom information through a preset medical consultation model based on the medical consultation information; A second application module is used to generate a complete symptom set of the patient through a preset dialectical model based on the symptom information; An information display module is used to combine the medical consultation record and the complete symptom set to form the pre-medical consultation information.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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