CN119381027A - A method, device, equipment and storage medium for generating pre-diagnosis information - Google Patents
A method, device, equipment and storage medium for generating pre-diagnosis information Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The application provides a method, a device, equipment and a storage medium for generating pre-consultation information, and relates to the technical field of medical information processing. A generation method of pre-consultation information comprises the steps of obtaining consultation information of a patient, obtaining a consultation record and symptom information of the patient through a preset consultation model based on the consultation information, generating a complete symptom set of the patient through a preset dialectical model based on the symptom information, and combining the consultation record and the complete symptom set to form the pre-consultation information. According to the embodiment of the application, the doctor can be simulated to conduct the initiative guiding type inquiry, and the accuracy of the inquiry is improved.
Description
Technical Field
The present application relates to the field of medical information processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for generating pre-consultation information.
Background
In the traditional Chinese medicine diagnosis and treatment process, a doctor can know the occurrence and development of diseases, diagnosis and treatment progress, current symptoms and other conditions related to the diseases through purposeful inquiry of patients or accompanying patients so as to diagnose the diseases.
At present, the general inquiry process is completed by a doctor or is replaced by a form filling mode, so that the efficiency is low. How to combine the traditional Chinese medicine inquiry and the computer artificial intelligence technology to accurately collect the symptom information of the patient before the doctor inquiry and improve the inquiry efficiency and the doctor diagnosis accuracy is a problem to be solved in the current field.
Disclosure of Invention
According to one aspect of the application, a method for generating pre-consultation information is provided, which comprises the steps of obtaining the consultation information of a patient, obtaining the consultation record and the symptom information of the patient through a preset consultation model based on the consultation information, generating a complete symptom set of the patient through a preset dialectical model based on the symptom information, and combining the consultation record and the complete symptom set to form the pre-consultation information.
According to some embodiments, the real doctor-patient interaction data is obtained before the patient's visit information is obtained, and the inquiry model is trained through the doctor-patient interaction data.
According to some embodiments, training the inquiry model through doctor-patient interaction data comprises generating inquiry reference data according to the doctor-patient interaction data, revising the inquiry reference data, and performing fine tuning training on the inquiry model through the revised inquiry reference data.
According to some embodiments, acquiring a patient's inquiry record and symptom information through a preset inquiry model based on the diagnosis information comprises guiding the inquiry model to execute an inquiry process through a preset inquiry prompt according to the diagnosis information to acquire patient's complaint information, wherein the complaint information comprises patient's medical history information, generating the inquiry record according to the complaint information, and extracting the symptom information from the complaint information.
According to some embodiments, extracting symptom information from complaint information includes obtaining symptom information of a patient from the complaint information through a consultation model, converting the symptom information into an external standard symptom set, and matching the external standard symptom set with a preset internal standard knowledge set to generate an internal standard symptom set.
According to some embodiments, generating a complete symptom set of a patient through a preset dialectical model based on symptom information comprises obtaining a predicted symptom type through the dialectical model based on an internal standard symptom set, obtaining a symptom set corresponding to the predicted symptom type according to an internal standard knowledge set, comparing the internal standard symptom set with the symptom set corresponding to the predicted symptom type to obtain a supplementary symptom set, pushing the supplementary symptom set to the patient, and generating the complete symptom set according to feedback of the patient.
According to some embodiments, pushing the supplemental symptom set to the patient and generating the complete symptom set based on feedback from the patient includes obtaining symptoms selected by the patient from the supplemental symptom set, and generating the complete symptom set based on the internal standard symptom set and the symptoms selected by the patient from the supplemental symptom set.
According to one aspect of the application, a pre-consultation information generating device is provided, which comprises an information acquisition module, a first application module, a second application module and an information display module, wherein the information acquisition module is used for acquiring the consultation information of a patient, the first application module is used for acquiring the consultation record and symptom information of the patient through a preset consultation model based on the consultation information, the second application module is used for generating a complete symptom set of the patient through a preset dialectical model based on the symptom information, and the information display module is used for combining the consultation record and the complete symptom set to form the pre-consultation information.
According to an aspect of the application there is provided an electronic device comprising one or more processors, storage means for storing one or more programs, which when executed by the one or more processors cause the one or more processors to carry out a method as hereinbefore described.
According to an aspect of the application, there is provided a computer readable storage medium having stored thereon a computer program or instructions which, when executed by a processor, implement a method as aforesaid.
According to the embodiment of the application, a doctor can be simulated by adopting a large model trained by doctor-patient interaction real data to conduct free, multipath and active guided inquiry, so that inquiry information is more complete and standard, and a more complete symptom set is obtained through a dialectical model and a syndrome knowledge set, so that the symptom extraction rate of a patient is higher and more accurate.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present application.
Fig. 1 shows a schematic diagram of a pre-interrogation system according to an example embodiment of the application.
Fig. 2 shows a flowchart of a method of generating pre-consultation information according to an exemplary embodiment of the present application.
Fig. 3 shows a block diagram of a pre-inquiry information generating apparatus according to an exemplary embodiment of the present application.
Fig. 4 shows a block diagram of an electronic device according to an example embodiment of the application.
Detailed Description
The user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of related data is required to comply with the relevant laws and regulations and standards of the relevant country and region, and is provided with corresponding operation entries for the user to select authorization or rejection.
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application can be practiced without one or more of the specific details, or with other methods, components, materials, devices, operations, etc. In these instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
The application provides a method, a device, equipment and a storage medium for generating pre-consultation information, which can perform active guided consultation through a model and improve the efficiency and accuracy of acquiring patient information and symptoms.
A method, apparatus, device and storage medium for generating pre-inquiry information according to an embodiment of the present application will be described in detail with reference to the accompanying drawings.
Fig. 1 shows a schematic diagram of a pre-interrogation system according to an example embodiment of the application.
As shown in FIG. 1, the pre-consultation system 100 includes a consultation model 110, a dialectical model 120, and an interactive interface 130.
Prior to entering the interrogation process, the pre-interrogation system 100 obtains the patient's input information at the interactive interface 130 and extracts the patient's visit information therefrom.
According to some embodiments, the pre-consultation system 100 may communicate with a hospital registration system through a preset interface to obtain patient visit information.
The inquiry model 110 enters an inquiry flow through a preset inquiry prompt (text or voice), and actively guides the patient to conduct inquiry interaction through the interaction interface 130 so as to acquire the complaint information of the patient.
According to some embodiments, the interview model 110 is a large medical model that has been trained with corpus data of medical books, basic medical terms, and the like, and is fine-tuned trained using interview reference data generated based on doctor-patient interaction data.
The inquiry model 110 generates inquiry records after summarizing and sorting the complaint information of the patient, and extracts the medical history information of the patient.
Further, the inquiry model 110 extracts patient's symptom information from the patient's complaint information and converts the patient's symptom information into an external standard symptom set through standard symptom term conversion.
The pre-interrogation system 100 matches the set of external standard symptoms with a preset set of internal standard knowledge to achieve alignment of internal and external symptoms to generate the set of internal standard symptoms.
The dialectic model 120 predicts the syndrome pattern based on the generated internal standard symptom set, recommends the syndrome pattern with the highest prediction probability from the prediction result, and acquires the symptom set corresponding to the predicted syndrome pattern through the internal standard knowledge set.
According to some embodiments, dialectic model 120 is a model built based on a traditional algorithmic model or a precise knowledge set.
The pre-consultation system 100 compares the internal standard symptom set with the symptom set corresponding to the predicted syndrome type, eliminates the selected symptoms of the patient in the internal standard symptom set, and obtains the possible symptoms of the patient as a supplementary symptom set.
The pre-consultation system 100 prompts the patient through the interactive interface 130 to select supplemental symptoms from among the supplemental symptom sets and generates a complete symptom set for the patient based on the patient's feedback.
The pre-consultation system 100 combines the consultation records with the complete symptom set of the patient and pushes the formed pre-consultation information to the doctor for the doctor to refer in the actual diagnosis and treatment process of the patient.
Fig. 2 shows a flowchart of a method of generating pre-consultation information according to an exemplary embodiment of the present application.
As shown in fig. 2, in step S100, the pre-consultation information generating device acquires the patient' S visit information.
For example, in step S100, the pre-inquiry information generating apparatus obtains the patient' S information about the visit from the relevant system (e.g., registration system) of the hospital through the information that the patient inputs by himself on the system interactive interface or through a preset interface.
According to some embodiments, the patient's visit information includes the category of visit (e.g., first visit/second visit) and personal information of the patient.
In step S200, based on the visit information, the pre-visit information generating device acquires the visit record and symptom information of the patient through a preset visit model.
For example, in step S200, the pre-inquiry information generating device obtains inquiry records and symptom information of the patient through a preset inquiry model that has been trained, based on the patient' S visit information.
Before the patient's visit information is obtained, the pre-consultation information generating device firstly obtains the real doctor-patient interaction data.
According to some embodiments, the doctor-patient interaction data includes real recorded data derived from communication between doctors and patients in various diseases and departments in a plurality of hospitals.
Further, the pre-inquiry information generating device processes the doctor-patient interaction data through a preset multi-mode large model and generates recording text data, and formats the recording text data through the multi-mode large model according to a preset prompt (text or voice) of the multi-mode large model to obtain inquiry and answer reference data.
The pre-consultation information generating device acquires labels and revisions of the consultation reference data by the professional with medical knowledge, and forms a revising sample of the consultation reference data. The pre-inquiry information generating device revises the revised sample of the inquiry reference data through the inquiry model according to a sample prompt preset by the inquiry model to obtain revised inquiry reference data. The revised question-answer reference data may cover all relevant departments of the hospital.
Further, the pre-consultation information generating device carries out fine tuning training on the consultation model through the revised consultation reference data, so that partial parameters of the consultation model are optimized, and the consultation model has the capability of guiding consultation for different disease types.
After the patient's visit information is acquired, the pre-consultation information generating device guides a consultation model to conduct a consultation flow corresponding to the patient's visit information through a preset consultation prompt so as to collect the patient's complaint information.
For example, the preset inquiry prompt may be "please make an inquiry according to the department and category of the patient, and the inquiry model makes an inquiry interaction with the patient through the system interactive interface according to the inquiry prompt, so as to obtain the complaint information including the medical history information of the patient through an active guiding type dialogue.
According to the complaint information of the patient, the pre-consultation information generating device can guide the consultation model to carry out induction and arrangement of information according to the preset information processing prompt so as to obtain the consultation record and the medical history information of the patient.
Further, the pre-consultation information generating means extracts symptom information of the patient from the complaint information through the consultation model and converts the symptom information of the patient into an external standard symptom set.
For example, symptom information of "me's belly discomfort" is expressed in the complaint information of the patient, and a consultation model trained by corpus data of medical books, basic medical terms, and the like contains standard symptom terms of "belly discomfort" corresponding to "me's belly discomfort". The pre-consultation information generating device converts the natural language of 'uncomfortable abdomen of me' into 'uncomfortable abdomen' through a preset term conversion prompt 'please convert symptoms in a dialogue into standard symptom terms', and guides a consultation model to obtain an external standard symptom set corresponding to symptom information of a patient.
The pre-consultation information generating device matches the external standard symptom set of the patient with a preset internal standard knowledge set to determine whether the external standard symptom converted by the consultation model is aligned with the internal standard symptom in the internal standard knowledge set, and generates an internal standard symptom set according to a matching result.
According to some embodiments, the pre-consultation information generating means may perform similarity matching of the external standard symptom set of the patient with a preset internal standard knowledge set. The pre-inquiry information generating device analyzes the similarity between the terms of the external standard symptoms and the similar term set of the internal standard symptoms through an inquiry model to obtain the internal standard symptom terms with the highest similarity probability, and the internal standard symptom terms are used as query or matching algorithm keywords called by the internal standard knowledge set and the model to realize the alignment from the spoken language symptom terms to the internal standard symptom terms.
For example, the term of the oral symptoms of "i am uncomfortable with the abdomen" may be interpreted as "abdominal discomfort" by a questioning model. The pre-inquiry information generating device analyzes the similarity word of the abdomen discomfort through the inquiry model to obtain abdomen, discomfort and the like, and inquires similar internal standard symptom terms such as abdominal pain, head discomfort, hand discomfort and the like according to the word segmentation information. The preliminary inquiry information generating device analyzes the similarity between "abdominal discomfort" and "abdominal pain", "head discomfort", "hand discomfort", etc. internal standard symptom terms through the inquiry model, and outputs "abdominal pain" having the highest similarity.
Further, the pre-inquiry information generating device acquires a correspondence between the spoken language symptom term "bellyband discomfort" and the internal standard symptom term "abdominal pain", and stores the correspondence. If the same type of spoken symptom terms reappear later, the pre-inquiry information generating device can directly interpret the same type of spoken symptom terms as bellyache according to the corresponding relation stored in the database, and analysis is not performed through the inquiry model.
In step S300, based on the symptom information, the pre-inquiry information generating means generates a complete symptom set of the patient through a preset dialectical model.
For example, in step S300, the pre-inquiry information generating means predicts the syndrome pattern by a preset dialectic model based on the internal standard symptom set corresponding to the symptom information of the patient, and generates the complete symptom set.
According to some embodiments, the dialectic model has been trained with a corresponding internal standard knowledge set prior to predicting the syndrome type.
The pre-inquiry information generating device inputs the internal standard symptom set into a trained dialectic model, and predicts the syndrome type corresponding to the internal standard symptom set through the dialectic model.
For example, the internal standard symptom set corresponding to the patient includes symptoms of "sore throat" and "headache", while the internal standard knowledge set for training the dialectical model is used for "sore throat" associated with "cold" and "headache" associated with "cold", so that the dialectical model predicts that the patient has a higher probability of "cold" after being matched by the algorithm. The dialectical model recommends "cold" as a predicted syndrome to the pre-inquiry information generating device.
The pre-inquiry 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.
For example, the dialectical model predicts that "cold" is also associated with symptoms of "fever" among the symptoms of the internal standard knowledge set. The pre-inquiry information generating device compares the predicted symptom sets of 'sore throat', 'headache' and 'fever' corresponding to 'cold' with the internal standard symptom set obtained through the inquiry model, determines that the internal standard symptom set does not contain symptoms of 'fever', and takes 'fever' as a complementary symptom set of a current patient.
The pre-consultation 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 symptom.
Further, the pre-consultation information generating device acquires the supplementary symptoms selected by the patient through the system interactive interface, and generates a complete symptom set of the patient by combining the internal standard symptom set.
In step S400, the pre-inquiry information generating apparatus combines the inquiry record and the complete symptom set to form pre-inquiry information.
For example, in step S400, the pre-inquiry information generating means forms pre-inquiry information in combination with the inquiry records obtained by the inquiry model and the complete symptom set obtained by the dialectic model.
According to some embodiments, the pre-consultation information generating device pushes the pre-consultation information to a doctor end for the doctor to refer in the diagnosis and treatment process.
According to the embodiment of the application, the technical scheme is not limited to a specific inquiry flow, and the doctor can be simulated through the model to conduct the initiative guiding inquiry, so that the inquiry information is more complete and standard, and the complete symptom set can be obtained through the dialectical model and the related knowledge set, so that the symptom extraction rate is improved.
Fig. 3 shows a block diagram of a pre-inquiry information generating apparatus according to an exemplary embodiment of the present application.
As shown in fig. 3, the pre-consultation information generating apparatus 200 is applied to a pre-consultation system, and includes an information acquiring module 210, a first application module 220, a second application module 230 and an information displaying module 240.
The information acquisition module 210 acquires the patient's visit information from the relevant system (e.g., registration system) of the hospital through the information input by the patient on the system interactive interface by himself or through a preset interface.
The first application module 220 obtains real doctor-patient interaction data before obtaining patient care information.
The first application module 220 processes the doctor-patient interaction data through a preset multi-mode big model, generates recording text data, and formats the recording text data through the multi-mode big model according to a preset prompt of the multi-mode big model to obtain question-answer reference data.
The first application module 220 obtains labels and revisions of the questionnaire reference data by professionals with medical knowledge and forms a revised sample of the questionnaire reference data.
The first application module 220 revises the revised sample of the question-answer reference data through the question model according to the sample prompt preset by the question model, and obtains revised question-answer reference data.
The first application module 220 performs fine tuning training on the questioning model through the revised questioning reference data.
After acquiring the patient's visit information, the first application module 220 guides the inquiry model to perform an inquiry process corresponding to the patient's visit information through a preset inquiry prompt, so as to collect patient's complaint information.
According to the complaint information of the patient, the first application module 220 can guide the inquiry model to conduct induction arrangement of information according to the preset information processing prompt so as to obtain inquiry records and medical history information of the patient.
The first application module 220 extracts symptom information of the patient from the complaint information through the inquiry model and converts the symptom information of the patient into an external standard symptom set.
The first application module 220 matches the external standard symptom set of the patient with a preset internal standard knowledge set, and generates an internal standard symptom set according to the matching result.
The second application module 230 inputs the internal standard symptom set into the trained dialectic model, and predicts the syndrome type corresponding to the internal standard symptom set through the dialectic model.
The second application module 230 compares the internal standard symptom set with the symptom set corresponding to the predicted syndrome pattern to obtain a supplemental symptom set for the current patient.
The second application module 230 pushes the internal standard symptom set and the supplemental symptom set to the patient through the system interactive interface and prompts the patient to select supplemental symptoms.
The second application module 230 obtains the supplemental symptoms selected by the patient through the system interactive interface and generates a complete symptom set of the patient in combination with the internal standard symptom set.
The information presentation module 240 combines the inquiry records obtained by the inquiry model and the complete symptom set obtained by the dialectic model to form pre-inquiry information.
The information display module 240 pushes the pre-consultation information to the doctor end by the pre-consultation information generating device.
Fig. 4 shows a block diagram of an electronic device according to an example embodiment of the application.
As shown in fig. 4, the electronic device 600 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present application.
As shown in fig. 4, the electronic device 600 is embodied in the form of a general purpose computing device. The components of electronic device 600 may include, but are not limited to, at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different system components (including memory unit 620 and processing unit 610), a display unit 640, and the like. In which a storage unit stores program codes that can be executed by the processing unit 610, so that the processing unit 610 performs the methods according to various exemplary embodiments of the present application described in the present specification. For example, the processing unit 610 may perform the method as shown in fig. 2.
The storage unit 620 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
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 or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing 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 using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the description of the embodiments above, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. The technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
The software product may employ 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 can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of a readable storage medium include an electrical connection having 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind 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., connected via the Internet using an Internet service provider).
The computer-readable medium carries one or more programs which, when executed by one of the devices, cause the computer-readable medium to perform the aforementioned functions.
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
The foregoing detailed description of the embodiments of the application has been presented only to assist in understanding the method and its core ideas of the application. Meanwhile, based on the idea of the present application, those skilled in the art can make changes or modifications on the specific embodiments and application scope of the present application, which belong to the protection scope of the present application. In view of the foregoing, this description should not be construed as limiting the application.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112015917A (en) * | 2020-09-07 | 2020-12-01 | 平安科技（深圳）有限公司 | Data processing method and device based on knowledge graph and computer equipment |
| CN113689951A (en) * | 2021-08-04 | 2021-11-23 | 翼健(上海)信息科技有限公司 | Intelligent diagnosis guiding method, system and computer readable storage medium |
| CN116864150A (en) * | 2023-06-29 | 2023-10-10 | 中电科新型智慧城市研究院有限公司 | Medical pre-consultation auxiliary information generation method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112015917A (en) * | 2020-09-07 | 2020-12-01 | 平安科技（深圳）有限公司 | Data processing method and device based on knowledge graph and computer equipment |
| CN113689951A (en) * | 2021-08-04 | 2021-11-23 | 翼健(上海)信息科技有限公司 | Intelligent diagnosis guiding method, system and computer readable storage medium |
| CN116864150A (en) * | 2023-06-29 | 2023-10-10 | 中电科新型智慧城市研究院有限公司 | Medical pre-consultation auxiliary information generation method, device, equipment and storage medium |
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