CN113161016A

CN113161016A - Intelligent medical service system, method and storage medium

Info

Publication number: CN113161016A
Application number: CN202011624191.0A
Authority: CN
Inventors: 樊代明; 钟南山; 姚娟娟
Original assignee: Shanghai Mingping Medical Data Technology Co ltd
Current assignee: Shanghai Mingping Medical Data Technology Co ltd
Priority date: 2020-12-31
Filing date: 2020-12-31
Publication date: 2021-07-23
Also published as: WO2022141925A1

Abstract

The invention discloses an intelligent medical service system, a method and a storage medium, wherein the intelligent medical service system comprises: the user side is used for inputting health information related to the user; the intelligent medical server is in communication connection with the user side and comprises an intelligent medical brain model; the intelligent medical brain model carries out health assessment on the user based on the health information related to the user to obtain a health assessment result; and the doctor end is in communication connection with the intelligent medical server end and is used for inputting doctor suggestion information based on the health assessment result and feeding back the doctor suggestion information to the user end. The intelligent medical service system is a comprehensive medical service system, can serve a user side, a doctor side, other doctor management sides, a database management side and the like, can integrate health related information of all users and doctor related information to form a comprehensive database, provides comprehensive monitoring management for a database manager, and finds abnormal information in time.

Description

Intelligent medical service system, method and storage medium

Technical Field

The invention belongs to the field of medical health data processing, and relates to an intelligent medical service system, an intelligent medical service method and a storage medium.

Background

As society develops, the medical health industry is being attended by more and more people. The medical health industry is in a rapid development stage from a global perspective. Along with the continuous improvement of the economic level of China, the attention degree of the masses to medical health is gradually improved, and the medical health industry of China begins to enter a high-speed development period.

Due to the particularity of medical health and the complexity of the system, the internet-based process in the medical health field is still in a relatively early stage compared with other fields. However, in terms of policies, society and technical environment, internet medical treatment has already met certain development conditions.

The application aims to provide an intelligent medical brain model to assist doctors in completing relevant diagnosis and treatment work so as to build a complete set of complete medical health service system based on the intelligent medical brain model.

Disclosure of Invention

The invention aims to provide an intelligent medical service system, an intelligent medical service method and a storage medium, which are used for realizing a complete set of medical health service system constructed based on an intelligent medical brain model.

In order to solve the technical problems, the invention is realized by the following technical scheme:

the present invention provides an intelligent medical service system, comprising: the user side is used for inputting health information related to the user; the intelligent medical server is in communication connection with the user side and comprises an intelligent medical brain model; the intelligent medical brain model carries out health assessment on the user based on the health information related to the user to obtain a health assessment result; and the doctor end is in communication connection with the intelligent medical server end and is used for inputting doctor suggestion information based on the health assessment result and feeding back the doctor suggestion information to the user end.

In an embodiment of the invention, the intelligent medical service system further includes: the doctor management terminal is used for inputting doctor management strategies, doctor evaluation strategies, doctor communication learning strategies or/and doctor cultivation strategies; the intelligent medical service terminal is in communication connection with the doctor management terminal, a doctor management subsystem is established according to the doctor management strategy, a doctor evaluation subsystem is established according to the doctor evaluation strategy, a doctor communication learning subsystem is established according to the doctor communication learning strategy, or/and a doctor cultivation subsystem is established according to the doctor cultivation strategy; the doctor end is in communication connection with the intelligent medical service end and is used for participating in the doctor management subsystem, the doctor evaluation subsystem, the doctor communication learning subsystem or/and the doctor cultivation subsystem.

In an embodiment of the invention, the intelligent medical service system further includes: the doctor terminal issues an inquiry case based on an inquiry model; and the doctor management subsystem in the intelligent medical service end searches matched doctor ends for solving the inquiry cases based on an inquiry matching strategy.

In an embodiment of the invention, the intelligent medical service system further includes: the user side issues a health self-test request to the intelligent medical service side; and the intelligent medical brain model in the intelligent medical server performs health self-test on the user based on the health information related to the user and feeds back the health self-test result to the user.

In an embodiment of the invention, the system for establishing the intelligent medical brain model includes: the neuron model setting module is used for setting the type and the attribute of the neuron model; the types of the neuron models comprise a disease neuron model and a disease evaluation factor neuron model; the neuron model generating module is connected with the neuron model setting module and used for generating a corresponding neuron model according to the type and the attribute of the neuron model; the disease response standard setting module is used for setting the disease response standard information; and the neuron model association module is respectively connected with the neuron model generation module and the disease response standard setting module, and establishes association relations between the disease neuron models and the disease evaluation factor neuron models according to the disease response standard information.

In an embodiment of the invention, the neuron model association module includes: a first association unit according to presetEstablishing association relation between each disease neuron model and each disease evaluation factor neuron model by initial self-definition of disease response standard information; or/and a second association unit for updating and establishing association W between each disease neuron model and each disease evaluation factor neuron model according to preset disease response standard information_j；

Wherein, W_jA correlation coefficient representing disease evaluation factor data j in the disease cause subset and corresponding disease type data; m is a positive integer representing the number of disease evaluation factor data in the disease cause subset, N_mRepresenting the number of disease type data associated with the disease evaluation factor data m, N_jRepresents the number of disease type data associated with the disease evaluation factor data j, and j ≦ M.

In an embodiment of the invention, the system for establishing an intelligent medical brain model further includes: and the disease treatment standard updating module is in communication connection with the disease treatment standard setting module and is used for adding, deleting or/and updating the disease treatment standard information.

In an embodiment of the invention, the neuron model association module further includes: a third correlation unit for updating and adjusting the correlation W between each disease neuron model and each disease evaluation factor neuron model according to the increased, deleted or/and updated disease coping standard information_j。

In an embodiment of the invention, the neuron model generating module includes: the neuron model input generation unit is used for generating a corresponding neuron model input function according to the type and the attribute of the neuron model; the neuron model processing and generating unit is used for generating a corresponding processing function of the neuron model according to the type and the attribute of the neuron model; and the neuron model output generation unit is used for generating a corresponding neuron model output function according to the type and the attribute of the neuron model.

In an embodiment of the present invention, when the neuron model generation module generates the disease neuron model, the neuron model input generation unit generates a corresponding disease neuron model input function according to an attribute of the disease neuron model; the attributes of the disease neuron model comprise the large class of diseases, the names of the diseases, the small class of the diseases and disease-related information; the neuron model processing and generating unit generates a processing function of a corresponding disease neuron model according to the attribute of the disease neuron model; and the neuron model output generation unit generates a corresponding disease neuron model output function according to the attribute of the disease neuron model.

In an embodiment of the present invention, when the neuron model generating module generates the disease evaluation factor type neuron model, the neuron model input generating unit generates a corresponding disease evaluation factor type neuron model input function according to an attribute of the disease evaluation factor type neuron model; the attributes of the disease evaluation factor neuron model comprise the category of the disease evaluation factor, the name of the disease evaluation factor, the grade of the disease evaluation factor and the related information of the disease evaluation factor; the neuron model processing and generating unit generates a processing function of the corresponding disease evaluation factor neuron model according to the attribute of the disease evaluation factor neuron model; the neuron model output generation unit generates a corresponding disease evaluation factor neuron model output function according to the attribute of the disease evaluation factor neuron model.

In an embodiment of the invention, the system for establishing an intelligent medical brain model further includes: the information acquisition module is in communication connection with the disease evaluation factor neuron model and used for acquiring a disease evaluation factor data set and inputting the disease evaluation factor data set into the disease evaluation factor neuron model; the disease acquisition module is in communication connection with the disease neuron model and acquires an output result of the disease neuron model; and the analysis processing module is respectively connected with the disease acquisition module and the disease response standard setting module, performs comparison analysis according to the output result of the disease neuron model and an expected result, and outputs a relevant instruction for judging whether the corresponding disease response standard information needs to be corrected.

The invention also provides an intelligent medical service method, which comprises the following steps: acquiring health information related to a user; utilizing an intelligent medical brain model; the intelligent medical brain model carries out health assessment on the user based on the health information related to the user to obtain a health assessment result; and acquiring doctor suggestion information based on the health assessment result and feeding back the doctor suggestion information to the user.

The present invention also provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the method of any one or several of the intelligent medical service method.

As described above, the intelligent medical service system, method and storage medium according to the present invention have the following advantages:

the intelligent medical service system is a comprehensive medical service system which can serve a user side, a doctor side, other doctor management sides, a database management side and the like. The intelligent medical service system can provide convenience services such as health information management backup and health self-test of a user, can also provide auxiliary services such as auxiliary diagnosis and treatment for doctors, saves doctor resources, improves diagnosis and treatment efficiency, and avoids diagnosis and treatment deviation or errors; related services for managing doctors can be provided for other doctor management terminals, such as learning communication services of doctors, skill training services and the like; the system can integrate health related information of all users and doctor related information to form a comprehensive database, can provide comprehensive monitoring management for a database manager, finds abnormal information in time and ensures healthy and orderly life of people.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic diagram of an exemplary implementation structure of an intelligent medical brain model building system according to the present invention;

FIG. 2 is a schematic diagram of an exemplary implementation structure of the neuron model association module according to the present invention;

fig. 3 is a schematic diagram of an exemplary implementation structure of the intelligent medical brain model building system according to the present invention;

FIG. 4 is a schematic diagram of an exemplary implementation of the neuron model generation module according to the present invention;

fig. 5 is a schematic diagram of an exemplary implementation structure of the intelligent medical brain model building system according to the present invention;

FIG. 6 is a schematic diagram of an exemplary implementation flow of the intelligent medical brain model building method according to the present invention;

FIG. 7 is a schematic diagram of an exemplary implementation structure of the intelligent medical service system according to the present invention;

FIG. 8 is an exemplary diagram of a directed acyclic graph of a matching medical expert;

FIG. 9 is a diagram of an exemplary association between health information;

FIG. 10 is a representation of the correlation model;

fig. 11 is a schematic flow chart of a method for calculating the probability of any suspected disease by the self-test report generation module.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

The intelligent medical brain model generated by the intelligent medical brain model establishing system can realize full discipline coverage, including the aspects of clinic (western medicine and traditional Chinese medicine), health management, medical skill, nursing, pharmacy, science popularization and the like, is convenient to cooperate with each team expert to establish a bottom layer structure, and is more authoritative.

The intelligent medical brain model carries out intelligent matching identification according to various symptoms, or/and indexes and other information of a patient, can judge the possibility of suffering from a certain disease, can send related identification information and result data to an expert, is analyzed by the expert, and if new data exist in the disease, the new data are added into a medical map database to form systematic enrichment, so far, the intelligent medical brain model disclosed by the invention already comprises more than 3000 common disease models.

The intelligent medical brain model generated by the invention can form a medical knowledge map database covering most common diseases at present based on big data statistical analysis results and combined with clinical experiences of top-level medical experts and academists in the industry.

The patient can carry out health self-test through the intelligent medical brain model generated by the invention, so that the hospitalizing cost of people can be saved, the problem of medical resource shortage can be relieved, the urgent requirements of the majority of patients on hospitalizing diagnosis can be met to the greatest extent, and the life quality of people is improved.

The doctor can pre-diagnose the patient through the intelligent medical brain model generated by the invention, assist the doctor to perform early full diagnosis and treatment, and give out own professional diagnosis by the doctor, so that the problem that the specialist neglects other aspects due to different specialties of medical knowledge can be avoided, the specialist can be assisted to accurately complete the professional diagnosis, and misdiagnosis is avoided.

Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.

Referring to fig. 1, an embodiment of the present invention provides an intelligent medical brain model building system, where the intelligent medical brain model building system 100 includes: the model setting module 110, the neuron model generating module 120, the disease response standard setting module 130 and the neuron model associating module 140.

The neuron model setting module 110 is configured to set a type and an attribute of a neuron model; the types of neuron models include a disease neuron model and a disease evaluation factor neuron model.

The attributes of the disease evaluation factor-based neuron model include the type and attributes of the disease evaluation factor. For example: the disease evaluation factors include symptoms (such as fever, cough and other physical signs), indexes (such as height, weight, body temperature, blood routine, blood pressure, blood fat, heart rate and other detection indexes), family genetic history, medication history, disease history and the like. Specifically, for example, for the symptom of fever, it includes attributes of low fever, high fever, and the like; the index of body temperature includes classification attributes such as 36-37 ℃, 37.3-38 ℃, 38.1-40 ℃ and more than 40 ℃.

Attributes of the disease-like neuron model include the type and subtype of the disease. For example: the types of diseases include hypertension, diabetes, gastritis, eczema, coronary heart disease, cardiomyopathy and the like; the disease subtype refers to a combination of symptoms, indices, familial inheritance, disease history, medication history, age and/or sex, etc., which can be used to determine the type of disease, for example: fever greater than 40 ℃ plus white blood cell count greater than 1000 may be considered a subtype of a disease, fever greater than 37 ℃ plus white blood cell count greater than 500 may be considered another subtype.

According to the embodiment, the disease neuron model and the disease evaluation factor neuron model are subdivided, so that the health condition of a patient can be intelligently and comprehensively checked, and a doctor is assisted to complete early diagnosis of accurate diagnosis.

The neuron model generating module 120 is connected to the neuron model setting module 110, and is configured to generate a corresponding neuron model according to the type and attribute of the neuron model.

In an embodiment of the present invention, the mathematical model expression of the neuron model may refer to the following types:

there are various forms for the excitation function f [ ] such as: step type, linear type, S type, and the like.

The disease coping standard setting module 130 is used for setting each disease coping standard information.

The neuron model association module 140 is connected to the neuron model generation module 120 and the disease response criterion setting module 130, respectively, and establishes association relationships between the disease neuron models and the disease evaluation factor neuron models according to the disease response criterion information.

In an embodiment of the present invention, referring to fig. 2, the neuron model association module 140 includes: a first association unit 141, a second association unit 142, or/and a third association unit 143.

The first association unit 141 initially and self-defines an association relationship between each disease neuron model and each disease evaluation factor neuron model according to preset disease response standard information.

The second association unit 142 updates and establishes an association relationship W between each disease neuron model and each disease evaluation factor neuron model according to preset disease response standard information_j；

In an embodiment of the present invention, referring to fig. 3, the intelligent medical brain model building system 100 further includes: disease management criteria update module 150. The disease treatment standard updating module 150 is communicatively connected to the disease treatment standard setting module 130, and is configured to add, delete, or/and update each piece of disease treatment standard information. The neuron model correlation module 140 further comprises: a third association unit 143. The third correlation unit 143 updates and adjusts the correlation W between each disease neuron model and each disease evaluation factor neuron model according to the disease coping standard information added, deleted, or/and updated_j。

In an embodiment of the present invention, referring to fig. 4, the neuron model generating module 120 includes: a neuron model input generation unit 121, a neuron model processing generation unit 122, and a neuron model output generation unit 123. The neuron model input generating unit 121 generates a corresponding neuron model input function according to the type and the attribute of the neuron model; the neuron model processing and generating unit 122 generates a processing function of a corresponding neuron model according to the type and the attribute of the neuron model; the neuron model output generation unit 123 generates a corresponding neuron model output function according to the type and attribute of the neuron model.

Specifically, when the neuron model generation module 120 generates the disease neuron model, the neuron model input generation unit 121 generates a corresponding disease neuron model input function according to the attribute of the disease neuron model; the attributes of the disease neuron model comprise the large class of diseases, the names of the diseases, the small class of the diseases and disease-related information; the neuron model processing generation unit 122 generates a processing function of the corresponding disease neuron model according to the attribute of the disease neuron model; the neuron model output generating unit 123 generates a corresponding disease neuron model output function according to the attribute of the disease neuron model.

Specifically, when the neuron model generation module 120 generates the disease evaluation factor-based neuron model, the neuron model input generation unit 121 generates a corresponding disease evaluation factor-based neuron model input function according to the attribute of the disease evaluation factor-based neuron model; the attributes of the disease evaluation factor neuron model comprise the category of the disease evaluation factor, the name of the disease evaluation factor, the grade of the disease evaluation factor and the related information of the disease evaluation factor; the neuron model processing generation unit 122 generates a processing function of the corresponding disease evaluation factor neuron model according to the attribute of the disease evaluation factor neuron model; the neuron model output generation unit 123 generates a corresponding disease evaluation factor-based neuron model output function from the attribute of the disease evaluation factor-based neuron model.

In an embodiment of the present invention, referring to fig. 5, the intelligent medical brain model building system 100 further includes: an information acquisition module 160, a disease acquisition module 170, and an analysis processing module 180.

The information acquisition module 160 is in communication connection with the disease evaluation factor neuron model, and acquires a disease evaluation factor data set to input the disease evaluation factor data set into the disease evaluation factor neuron model;

the disease acquisition module 170 is in communication connection with the disease neuron model and acquires an output result of the disease neuron model; the analysis processing module 180 is connected to the disease acquisition module 160 and the disease response standard setting module 130, respectively, and performs comparison analysis according to the output result of the disease neuron model and an expected result, and outputs a relevant instruction indicating whether the corresponding disease response standard information needs to be corrected.

The intelligent medical brain model can be automatically configured according to medical information which is input by medical experts and related to diseases, so that the intelligent medical diagnosis and treatment model of each disease is generated, the association relation of the medical information which is related to each disease is continuously established or/and updated in the running process of the intelligent medical diagnosis and treatment model of each disease, and a comprehensive medical knowledge map is gradually formed, so that the intelligent medical brain model has great guiding significance for follow-up medical teaching research.

Referring to fig. 6, an embodiment of the present invention further provides an intelligent medical brain model building method, including:

s601, setting the type and the attribute of the neuron model; the types of neuron models include a disease neuron model and a disease evaluation factor neuron model.

S602, generating a corresponding neuron model according to the type and the attribute of the neuron model.

S603, standard information corresponding to each disease is set.

S604, establishing the association relation between each disease neuron model and each disease evaluation factor neuron model according to the standard information corresponding to each disease.

In an embodiment of the present invention, the implementation process of the step S604 of establishing the association relationship between the disease neuron models and the disease evaluation factor neuron models according to the disease correspondence standard information includes: establishing association relations between the neuron models of the diseases and the neuron models of the disease evaluation factors in an initial self-defined manner according to preset disease coping standard information; or/and updating and establishing the incidence relation W between each disease neuron model and each disease evaluation factor neuron model according to preset disease response standard information_j；

Wherein, W_jRepresenting the subset of causes of said diseaseThe correlation coefficient of the disease evaluation factor data j and the corresponding disease type data; m is a positive integer representing the number of disease evaluation factor data in the disease cause subset, N_mRepresenting the number of disease type data associated with the disease evaluation factor data m, N_jRepresents the number of disease type data associated with the disease evaluation factor data j, and j ≦ M.

In an embodiment of the invention, the method for building an intelligent medical brain model may further include: adding, deleting or/and updating each disease coping standard information.

In an embodiment of the present invention, the implementation process of the step S604 of establishing the association relationship between the disease neuron models and the disease evaluation factor neuron models according to the disease correspondence standard information further includes: updating and adjusting the association relation W between each disease neuron model and each disease evaluation factor neuron model according to the added, deleted or/and updated disease coping standard information_j。

In an embodiment of the present invention, an implementation process of the step S602 of generating the corresponding neuron model according to the type and the attribute of the neuron model includes: generating a corresponding neuron model input function according to the type and the attribute of the neuron model; generating a processing function of the corresponding neuron model according to the type and the attribute of the neuron model; and generating a corresponding neuron model output function according to the type and the attribute of the neuron model.

In an embodiment of the present invention, an implementation process of the step S602 of generating the corresponding neuron model according to the type and the attribute of the neuron model includes: when the generation module generates the disease neuron model, generating a corresponding disease neuron model input function according to the attribute of the disease neuron model; the attributes of the disease neuron model comprise the large class of diseases, the names of the diseases, the small class of the diseases and disease related information; generating a processing function of the corresponding disease neuron model according to the attribute of the disease neuron model; and generating a corresponding disease neuron model output function according to the attribute of the disease neuron model.

In an embodiment of the present invention, an implementation process of the step S602 of generating the corresponding neuron model according to the type and the attribute of the neuron model includes: when the disease evaluation factor neuron model is generated, generating a corresponding disease evaluation factor neuron model input function according to the attribute of the disease evaluation factor neuron model; the attributes of the disease evaluation factor neuron model comprise the category of the disease evaluation factor, the name of the disease evaluation factor, the grade of the disease evaluation factor and the related information of the disease evaluation factor; generating a processing function of the corresponding disease evaluation factor neuron model according to the attribute of the disease evaluation factor neuron model; and generating a corresponding disease evaluation factor neuron model output function according to the attribute of the disease evaluation factor neuron model.

In an embodiment of the invention, the method for building an intelligent medical brain model further includes: collecting a disease evaluation factor data set and inputting the data set into a neuron model of the disease evaluation factor; collecting an output result of the disease neuron model; and comparing and analyzing the output result of the disease neuron model and an expected result, and outputting a relevant instruction for judging whether the corresponding disease coping standard information needs to be corrected.

The embodiment of the invention also provides a service system which comprises the intelligent medical brain model establishing system.

An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program implements the intelligent medical brain model building method according to the present invention.

Referring to fig. 7, an embodiment of the present invention further provides an intelligent medical service system 700, where the intelligent medical service system 700 includes: the intelligent medical server 710, the user terminal 720 and the doctor terminal 730.

The user terminal 720 is used for inputting health information related to the user. The health information includes personal basic information, physical examination information (health-related information that can be detected by hospitals, such as blood routine, blood pressure, heart rate, etc.), disease history, genetic history, medication history, etc.

The intelligent medical server 710 is in communication connection with the user 720 and comprises an intelligent medical brain model; and the intelligent medical brain model carries out health assessment on the user based on the health information related to the user to obtain a health assessment result.

The doctor end 730 is in communication connection with the intelligent medical service end 710, and is used for inputting doctor suggestion information based on the health assessment result and feeding back the doctor suggestion information to the user end. When the health assessment result given by the intelligent medical service end 710 is accurate, the user end can be automatically replied without further diagnosis by a doctor; when the health assessment result is not ideal enough, the doctor can make a professional diagnosis, so that the medical resources can be saved, and the diagnosis accuracy and comprehensiveness of the doctor can be improved.

In an embodiment of the present invention, referring to fig. 7, the intelligent medical service system 700 further includes: the physician administration end 740.

The doctor management terminal 740 is used for inputting a doctor management strategy, a doctor evaluation strategy, a doctor communication learning strategy or/and a doctor training strategy.

The intelligent medical service end 710 is in communication connection with the doctor management end 740, a doctor management subsystem 711 is established according to the doctor management strategy, a doctor evaluation subsystem 712 is established according to the doctor evaluation strategy, a doctor communication learning subsystem 713 is established according to the doctor communication learning strategy, or/and a doctor cultivation subsystem 714 is established according to the doctor cultivation strategy. The doctor end 730 is communicatively connected to the intelligent medical service end 710, and is configured to participate in the doctor management subsystem 711, the doctor evaluation subsystem 712, the doctor communication learning subsystem 713, or/and the doctor training subsystem 714.

In an embodiment of the invention, the intelligent medical service system further includes: the doctor end issues an inquiry case based on an inquiry system; and the doctor management subsystem in the intelligent medical service end searches matched doctor ends for solving the inquiry cases based on an inquiry matching strategy.

Further, the inquiry system can help the doctor to answer the medical information quickly, so that the medical skill level of the doctor can be improved. The inquiry system comprises a rule configuration module, a first communication module, an information screening module, an information matching module, a second communication module and a storage module.

The rule configuration module is used for configuring inquiry rules and inquiry priorities, the first communication module is used for receiving medical information sent by a user, the user can upload the medical information according to the inquiry rules, and the user can be a doctor. The information screening module is used for screening out effective medical information according to the medical information and establishing a medical information model according to the effective medical information. The information matching module is used for matching the medical experts for the effective medical information according to the inquiry priority. The second communication module is used for sending the effective medical information and the medical information model to the medical expert, waiting for the response of the medical expert, modifying the medical information model by the medical expert, and the storage module 160 is used for storing the medical information, the effective medical information and the medical information model.

In this embodiment, the picture recognition unit may be used to receive an image of an affected part of a patient acquired by an external image acquisition device (e.g., a mobile phone or a computer), and then perform a preprocessing operation, and then send the preprocessed image of the affected part of the patient to the picture recognition detection unit. The picture identification detection unit is connected with the picture identification unit and is used for receiving the affected part image of the patient sent by the picture identification unit, extracting and identifying the affected part image information of the patient needing diagnosis and then sending the information to the medical knowledge base. In this embodiment, the external image capturing device may be any device having an image capturing and transmitting function, such as a mobile phone, a tablet computer, or a computer. In the picture identification unit, the preprocessing operation is preferably an illumination compensation operation, so that the quality of the image of the affected part of the patient is improved through illumination compensation, and finally, the preprocessed image is transmitted to the picture identification detection unit, and identification and feature extraction are carried out in the picture identification detection unit.

In this embodiment, the medical related information may also be uploaded through a text editing unit, the text editing unit is further connected to an information auxiliary input unit, a health information standard word is provided in the information auxiliary input unit, and the auxiliary information input unit sends the health information standard word to the user to prompt the user to input. For example, when the user inputs the word "hair" through the text editing unit, the information auxiliary input unit displays the prompt words such as "heat", "inflammation" or "swelling" for the user to select. When the user uploads the medically related information through the word programming unit, the medically related information is then sent to the medical knowledge base.

In this embodiment, after the user uploads the medical information through the second communication module, the information filtering module may filter the medical information. The information screening module comprises a first screening submodule, a second screening submodule and an identification submodule. The first screening submodule is used for directly screening effective medical information from the medical information, if the first screening submodule cannot directly screen the effective medical information from the medical information, the second screening submodule splits the medical information into the medical sub-information and screens the effective medical information from the medical sub-information, if the first screening submodule and the second screening submodule cannot screen the effective medical information, the medical information can be identified as invalid medical information through the identification submodule, and then the medical information is fed back to a user through the first communication module, so that the user can upload the medical information again.

In this embodiment, the rule configuration sub-module may configure the query rule and the query priority for the user. The rule configuration submodule may configure the query priority corresponding to each of the valid medical information for each rule, for example, based on each rule; wherein the rules are traditional Chinese medicine inquiry rules, western medicine inquiry rules, pharmacy inquiry rules, nursing inquiry rules, health management inquiry rules, public health inquiry rules and the like; wherein the inquiry priority is symptom priority and index priority. In the present embodiment, the symptom priority may be greater than the index priority, that is, the index information may be selected according to the symptom information.

The rule configuration submodule is provided with, for example, a traditional Chinese medicine inquiry rule, a pharmacy inquiry rule, a western medicine inquiry rule, a nursing inquiry rule, a medical technology inquiry rule, a health management inquiry rule and a public health inquiry rule. The user may select at least one query rule to upload medical information, for example, the user may select a traditional Chinese query rule and a Western query rule simultaneously. In this embodiment, the user is a doctor.

When the user selects the western medicine inquiry rule, the effective medical information screened by the information screening module can be embodied in the basic information unit, the symptom unit and the index unit, and meanwhile, the user further directly changes the information in the basic information unit, the symptom information and the index unit.

In this embodiment, the basic information module is used to fill in basic information of the patient, such as sex, age, place of residence, and work of the patient. Of course, the basic information module 1031 may also fill in patient's disease history, family history, medication history, allergy history, etc. For example, if a patient has a history of hypertension and a history of heart disease, the basic information module may add prompt information related to hypertension and heart disease to the basic information module based on the history of hypertension and heart disease, thereby improving the basic information module and helping a medical specialist to make a decision for preparation. In the present embodiment, if the environment of the residence of the patient is poor, the environment is also likely to cause diseases to the patient, for example, the lungs of the patient are likely to be infected, assuming that the residence of the patient is dusty. If the patient is in the dangerous industry for a long time, the work of the patient can easily cause diseases for the patient, for example, if the patient works in the heavy metal industry for a long time, the heavy metal in the blood of the patient is easy to exceed the standard.

In the embodiment, the symptom unit and the index unit are associated, a large number of symptom tags are arranged in the symptom unit, and a large number of abnormal index tags are arranged in the index unit. In this embodiment, the symptom label of the symptom unit includes relevant symptom signs displayed on the body or mind of the patient, such as: fever, dry cough, asthenia, dyspnea, etc. The abnormality index tag in the index unit includes physical indicators describing the physical condition of the patient in a quantitative manner, such as: blood pressure, body temperature, white blood cell count, hemoglobin, etc. In this embodiment, during the diagnosis of the patient by the doctor, the symptom label and the abnormality index label of the patient can be obtained.

When the user is uploading medical information, at least one symptom tag may be selected within the symptom unit, for example, symptom tag 1, the symptom tag 1 being, for example, fever. When the user selects the symptom tab 1, the index unit displays a plurality of abnormal index tabs, for example, an abnormal index tab 1, an abnormal index tab 2, and an abnormal index tab 3. The abnormality index tag 1 is, for example, blood pressure, the abnormality index tag 2 is, for example, body temperature, the abnormality index tag 3 is, for example, white blood cell count, and the abnormality index tag 4 is, for example, hemoglobin. The user can select a plurality of abnormal index tags according to the medical related information of the patient, and parameters of the abnormal index tags can be uploaded at the same time. In the present embodiment, the symptom unit is associated with the index unit, that is, when the number of symptom tags in the symptom unit changes, the number of abnormality index tags in the index unit also changes, that is, when any one symptom tag is determined, a plurality of abnormality index tags associated with the symptom tag are displayed in the index unit.

The information screening module also comprises a model establishing unit and a model updating unit, and after the information screening module screens out effective medical information or a user changes the medical information, the model establishing unit can establish a medical information model according to the symptom label and the abnormal index label. When the valid medical information is updated, the model establishing unit updates the medical information model.

In this embodiment, when the user uploads the symptom tag 1, and uploads the abnormal index tag 1, the abnormal index tag 2, the abnormal index tag 3, and the abnormal index tag 4, the model establishing unit establishes the medical information model. The medical information model takes the symptom label 1 as a core, and establishes association between the symptom label 1 and the abnormal index labels 1 to 4 according to the association relation. Specifically, for any symptom tag and any abnormal index tag, if the symptom tag causes the index tag to be abnormal, the symptom tag and the abnormal index tag are considered to have an association relationship, otherwise, the symptom tag and the abnormal index tag are considered to have no association relationship. For example, when the symptom label selected by the user is hypertension, the abnormal index label 1 selected by the user is hypertension, and the abnormal index label 2 is bleeding, it indicates that the symptom label 1 and the abnormal index label 1 are in a correlation relationship, that is, a medical information model of the symptom label 1, the abnormal index label 1, and the abnormal index label 2 can be established.

In this embodiment, when the medical information uploaded by the user changes or the symptom label selected by the user changes, the model updating unit updates the medical information model. Specifically, when the user selects the symptom tab 1 and the symptom tab 2, the number of abnormality index tabs also changes, and thus the medical information model also changes. The medical information model also takes the symptom label as a core, the symptom label 1 and the symptom label 2 have an association relationship, and the symptom label 1 and the symptom label 2 establish the association relationship through the abnormal index label 4. For example, the symptom label 1 is fever, the symptom label 2 is cough, and the abnormality index label 4 is blood-related abnormality. When a patient is febrile, blood routine abnormality occurs, and when a patient is coughed, blood routine abnormality occurs, so that fever and coughs can be associated by the blood routine abnormality.

In this embodiment, after the information filtering module filters out the valid medical information, the information matching module may match the medical expert according to the valid medical information. The information matching module may include a matching sub-module, a judging sub-module, and a priority configuring sub-module. The matching submodule can match medical experts according to the valid medical information, then the judging submodule judges whether the number of the matched medical experts is one, if the matching submodule only matches one medical expert, the matching submodule can wait for the medical expert to answer the valid medical information, if the number of the matched medical experts of the matching submodule is two or more, the matching submodule configures the medical expert priorities for the medical experts through the priority configuration submodule, and the medical expert priorities can comprise medical disciplines, doctor levels, areas where doctors are located, doctor workload, doctor activity degree and the like. For example, the medical disciplines may have a priority greater than the medical workload and the doctor level may have a priority greater than the doctor activity level. The priority configuration sub-module may also configure the medical expert priority based only on the medical discipline, medical level. For example, if the medical discipline of medical expert a is the same as the discipline to which the valid medical information belongs and the medical discipline of medical expert B is different from the discipline to which the valid medical information belongs, the priority of medical expert a is greater than the priority of medical expert B. It should be noted that, the higher the default doctor level of the present application is, the higher the medical skill level of the doctor is, and the wider the medical knowledge of the doctor is. It should be noted that the content of the medical expert's reply to the valid medical information may include a diagnosis idea, a diagnosis suggestion and a medical point. The diagnostic ideas, diagnostic recommendations and medical gist can be stored in a storage module.

As shown in fig. 8, which is a directed acyclic graph matching a medical expert. After the user uploads the symptom information through the first communication module, the symptom information may include other information such as the disease of the case, the subject of the case, the hospital of the case, the location of the case, and the like. The information matching module can match corresponding medical experts for the symptom information, and the inquiry system can form a plurality of execution paths according to the pre-configured priority, so that the most suitable medical experts are matched for the user. For example, taking the node a1 as a root node, the execution paths formed by the query system may include a1-a2-A3-a4, a1-a2-a5-a4, a1-A6-a4, and a1-A6-a7, and the execution paths are configured with different search rules. For example, after the user uploads symptom information (i.e., symptom information is input at root node a 1), the query system starts to search node a2 and node A6, and if a medical expert matching the symptom information is searched at node a2, node A3 and node A5 continue to be searched, for example, if a medical expert on line is searched at node A3, node A4 continues to be searched, a medical expert matching the symptom information is searched at node A4, if a medical expert on line is not searched at node A3, node a2 returns and node A5 is searched, and if a medical expert on line is searched at node A5, node A4 is searched, so that a medical expert matching the symptom information is searched at node A4. Similarly, if no medical specialist is matched in the node a4, return to the node A3 or a5, if the nodes A3 and a5 do not search for related information, return to the node a2, then return to the node a1, and search for the nodes a6 and a7 until a medical specialist matching the symptom information is searched.

In some embodiments, the interrogation system may also include an doubt module and a push module. When a user has a question about the medical information model, the relevant question may be recorded in the suspect module and then answered by the medical professional. When the user asks questions about the content answered by the doctor, the questions can be asked to the medical expert through the questioning module, so that the communication between the user and the medical expert is enhanced, and the medical skill level of the user can be improved. When the new user uploads the medical information, if the medical information uploaded by the new user is the same as the medical information in the medical information storage module, the pushing module can push the medical information model, the effective medical information and the content replied by the medical expert to the new user, so that the medical expert can be prevented from solving the same medical information.

In an embodiment of the invention, the intelligent medical service system further includes: the doctor end issues a query case based on a query system to carry out answering; and the doctor management subsystem in the intelligent medical service end searches matched doctor ends for solving the inquiry cases based on an inquiry matching strategy.

The solution system may include a priority configuration module, a first communication module, a selection module, a second communication module, a third communication module, and a storage module. The priority configuration module can be used for configuring the priority of the medical experts, the first communication module can be used for acquiring medical information, the selection module can select the medical experts according to the priority of the medical experts, the second communication module can send the medical information to the selected medical experts, if the medical experts cannot answer the medical information, the medical information can be sent to the medical experts with higher priority through the third communication module, and the storage module can be used for storing the medical information and the content answered by the medical experts.

In this embodiment, the user may upload medical information through the first communication module, and the user may select a chinese medical query rule, a pharmacy query rule, a western medical query rule, a nursing query rule, a medical technical query rule, a health management query rule, and a public health query rule. The user may select at least one query rule to upload medical information, e.g., the user may select a medico-medical query rule and a western-medical query rule simultaneously. In this embodiment, the user is a doctor, for example, a primary doctor. The reply information of the medical specialist may include diagnosis idea information, diagnosis advice information, and medical point information. The response information of the medical professional may be stored in the storage module.

By configuring the priority of the medical experts, after a user uploads medical information through the first communication module, a plurality of medical experts can be selected according to the medical information and the priority of the matched medical experts, the medical experts are sequentially arranged according to the priority order, then the medical information is sent to the medical experts through the second communication module, and if the medical experts cannot answer the medical information, the medical experts can send the medical information to medical experts with higher priority through the third communication module. When a doctor encounters medical problems which cannot be solved, the medical information can be uploaded to an answer system, then a more professional medical expert answers the medical information, and the answer content is sent to the doctor through a feedback module, so that the medical skill level of the doctor can be quickly improved.

In an embodiment of the invention, the intelligent medical service system further includes: the user side issues a health self-test request to the intelligent medical service side; and the intelligent medical brain model in the intelligent medical server performs health self-test on the user based on the health information related to the user and feeds back the health self-test result to the user. The user side and the intelligent medical service side jointly complete the health self-testing function, and the structure for realizing the function can be called a health self-testing system.

Further, the health self-test system comprises: the self-testing template generating module (which can be arranged at a user end) is used for generating a self-testing template; the self-testing template is used for prompting a user to input health information; the health information receiving module (which can be arranged at the intelligent medical server) is used for receiving the health information input by the user; the self-testing template updating module (which can be arranged at the intelligent medical server) is connected with the self-testing template generating module and the health information receiving module and is used for updating the self-testing template according to the health information of the user; the updated self-testing template is used for prompting the user to continuously input health information; and the self-test report generating module (which can be arranged at the intelligent medical server) is connected with the health information receiving module and is used for generating the self-test report of the user according to the health information of the user. Therefore, the health self-testing system generates a self-testing template through a self-testing template generating module to prompt a user to input health information; and the self-test template updating module can update the self-test template according to the health information which is input by the user, so that the user is prompted to input more health information. The health self-testing system does not need a user to select a target disease to be tested according to the self condition, and the user only needs to input corresponding health information according to the prompt of the self-testing template, so that the health self-testing system can solve the technical problem that the user needs to select the target disease to be tested according to the self condition in the existing scheme, and is beneficial to the quick and convenient realization of the health self-testing of the user.

In an embodiment of the present invention, the self-test template generating module is configured to generate a self-test template; the self-testing template generated by the self-testing template generating module is an initial self-testing template and is used for prompting a user to input health information; the health information includes physical condition information, psychological condition information, and/or lifestyle information of the user, etc. Preferably, the self-testing template is a general self-testing template generated by the self-testing template generating module according to the current season and the disease prevalence state, or is a personalized self-testing template generated by the self-testing template generating module according to different conditions of each user. The health information receiving module is used for receiving the health information input by the user. The self-test template updating module is connected with the self-test template generating module and the health information receiving module and used for updating the self-test template according to the health information of the user; and the updated self-testing template is used for prompting the user to continuously input the health information. And the self-test report generating module is connected with the health information receiving module and is used for generating a self-test report of the user according to the health information of the user.

In an embodiment of the present invention, the self-test template updating module is further configured to obtain an association between a plurality of health information, where the association may be represented by an association graph shown in fig. 9. Wherein the association between the plurality of health information may be obtained by statistics, such as: if the health information of 900 out of 1000 patients includes both health information 1 and health information 2, it indicates that there is a relationship between health information 1 and health information 2. Alternatively, the association between the plurality of health information may be defined according to medical knowledge, such as: fever is generally accompanied by cough, indicating that there is a correlation between fever and cough.

In this embodiment, the self-test template updating module may update the self-test template according to the association between the plurality of health information. Specifically, the self-test template updating module acquires all health information associated with the health information I according to the health information I of the user, deletes the currently acquired health information of the user as to-be-prompted information, and calculates a weight value of each health information in the to-be-prompted information. Based on this, the self-test template updating module 23 updates the self-test template according to the weight value of each piece of health information in the information to be prompted, for example: and the updated self-testing template prompts the user to input all health information contained in the information to be prompted, and preferentially prompts the user to input the health information with high weight value. For any health information A in the information to be prompted, the calculation method of the weight value R (A) is as follows:

wherein p is_AThe probability that the user continues to input other health information after inputting the previous health information is represented, the numerical value can be obtained by counting the input conditions of a plurality of users, and the value range is 0-1; n is the number of all health information associated with the health information a; r (B)_i) Indicating the ith health information B associated with the health information A_iWeight value of (A), Num (B)_i) Presentation and health information B_iThe amount of all health information associated. In specific application, the weight value of each piece of health information in the information to be prompted can be set to be 1, and the weight value of each piece of health information is updated in a 1-time or multi-time iteration mode; wherein, each iteration can obtain a value of R (A), and the specific iteration times can be set according to actual requirements。

The above process will be described in detail by way of an example. If the currently acquired health information I of the user comprises health information 1 and health information 2, the information to be prompted comprises health information 3, health information 4 and health information 6.

In the first iteration process, the weight values of all the health information are all 1, and p is taken_AWhen the value is 0.5, the weight of the health information 3 is

The health information 4 has a weight value of

The weight value of the health information 6 is

Based on the iteration, the updated health template can prompt the user to input health information 3, health information 4 and health information 6, and the prompt priority is as follows: health information 4>Health information 6>Health information 3.

It should be noted that, in a specific application, the weight value of each piece of health information may also be recalculated according to the weight value obtained after the first iteration to complete the second iteration, and the priority for prompting the user is determined according to the weight value obtained after the second iteration. Further, a third iteration may be performed on the basis of the second iteration, and so on. In this process, the higher the number of iterations, the more accurate the calculation of the priority, but the more the calculation amount increases.

In an embodiment of the present invention, after receiving the initial self-test template generated by the self-test template generation module, the user inputs and sends the corresponding first health information according to a prompt of the initial self-test template. And after the health information receiving module receives the first health information, the self-test report generating module generates a self-test report of the user according to the first health information. The first health information refers to health information input by a user according to the prompt of the initial self-testing template.

In an embodiment of the present invention, after receiving the initial self-test template generated by the self-test template generation module, the user inputs and sends the corresponding first health information according to a prompt of the initial self-test template. And after the health information receiving module receives the first health information, the self-test template updating module updates the initial self-test template according to the first health information and obtains a first updated template. And after receiving the first updating template, the user inputs corresponding second health information and sends the second health information. And after the health information receiving module receives the second health information, the self-test report generating module generates a self-test report of the user according to the second health information. The second health information refers to health information input by a user according to the prompt of the first updating template.

In an embodiment of the present invention, after receiving the initial self-test template generated by the self-test template generation module, the user inputs and sends the corresponding first health information according to a prompt of the initial self-test template. And after the health information receiving module receives the first health information, the self-test template updating module updates the initial self-test template according to the first health information and obtains a first updated template. And after receiving the first updating template, the user inputs corresponding second health information and sends the second health information. And after the health information receiving module receives the second health information, the self-test template updating module updates the first updating template according to the second health information and obtains a second updating template. And after receiving the second updating template, the user inputs corresponding third health information and sends the third health information. And after the health information receiving module receives the third health information, the self-test report generating module generates a self-test report of the user according to the third health information. And the third health information is health information input by the user according to the prompt of the second updating template.

In an embodiment of the invention, the self-test template generating module generates the personalized self-test template of the user according to the health profile and/or the historical self-test report of the user. Wherein, the health record of the user comprises sex, age, residence, disease history, genetic history, allergy history, medication history and the like. For example, if a health profile of a certain user describes a history of hypertension and a genetic history of heart disease of the user, the self-test template generating module may add prompt information related to hypertension and heart disease to the self-test template according to the genetic history and the disease history, thereby generating a personalized self-test template for the user.

In an embodiment of the present invention, the health information includes symptom sub-information, index sub-information and/or file sub-information; the self-testing template comprises a symptom self-testing sub-template, an index self-testing sub-template and/or an archive information sub-template; the symptom self-testing sub-template is used for prompting a user to input symptom sub-information, the index self-testing sub-template is used for prompting the user to input index sub-information, and the file detail information sub-template is used for prompting the user to input file sub-information.

Specifically, the self-testing template comprises a symptom self-testing sub-template, an index self-testing sub-template and/or an archive information sub-template; the symptom self-testing sub-template is used for prompting a user to input symptom sub-information, the index self-testing sub-template is used for prompting the user to input index sub-information, and the file information sub-template is used for prompting the user to input file sub-information.

In particular, the symptom sub-information includes relevant symptom signs physically or psychologically exhibited by the user, such as: fever, dry cough, hypodynamia, dyspnea and the like, and the user can determine the symptom sub-information through the physical expression of the user. The indicator sub-information comprises physical indicators describing the physical condition of the user in a quantitative manner, such as: blood pressure, body temperature, white blood cell count, hemoglobin, etc.; the user can obtain the index sub-information through corresponding medical equipment, and can also obtain the index sub-information through hospital physical examination and other modes. The profile sub-information includes relevant information in the user's health profile, such as: sex, age, place of residence, history of disease, genetic history, history of allergy, history of medication, etc.

In an embodiment of the present invention, the self-test template is configured to include an index self-test sub-template, a symptom self-test sub-template, and a profile information sub-template; at this time, the self-testing template can prompt the user to input corresponding index sub-information, symptom sub-information and archive sub-information, and the health self-testing system can generate a self-testing report of the user according to the symptom sub-information, the index sub-information and the symptom sub-information. According to the configuration mode, on one hand, the information integrity of the self-test report is improved; on the other hand, when the user is in the process of seeing a doctor, the doctor can directly obtain the symptom sub-information, the index sub-information and the archive sub-information of the user through the self-testing report, and the health self-testing system is connected with the actual diagnosis process of the doctor.

In an embodiment of the present invention, the health self-test system further includes a correlation model (i.e. the neuron model correlation module 140). The association model takes diseases as a core and establishes association between each disease and corresponding disease information according to an association relation. Specifically, the method comprises the following steps:

for any disease and any symptom sign, if the disease causes the symptom sign to appear on a user, the disease and the symptom sign are considered to have an association relationship; if the disease does not cause the symptom sign to appear in the user, the disease and the symptom sign are considered to have no association relation; therefore, in the diagnosis process, if the user has the symptom sign, the user can be considered to have the disease according to the association relationship. For example, patients commonly suffer from fever during cold, so that the cold diseases and the fever symptoms have an association relationship; in the diagnosis, when a patient has fever, it is considered that the patient may have a cold.

Regarding any disease and any examination index, if the disease causes the examination index to be abnormal, the disease and the examination index are considered to have a correlation, otherwise, the disease and the examination index are considered to have no correlation; therefore, in the diagnosis process, if the examination index of the user is abnormal, the user can be considered to be possibly suffered from the disease according to the association relation. For example, hypertension may cause the blood pressure value of a patient to exceed the normal value range, so that the hypertension disease and the blood pressure value index have a correlation relationship; in the diagnosis process, when the blood pressure value index of the patient is too high, the patient can be considered to have hypertension.

Regarding any disease and any profile related information, if the profile related information may cause the user to have the disease, considering that the disease and the profile related information have an association relation, otherwise, considering that the disease and the profile related information have no association relation; therefore, in the diagnosis process, if the user is found to have the profile-related information, the user can be considered to have the disease according to the association relationship. For example, the genetic history of heart disease increases the probability that a patient will have heart disease, and thus it can be considered that there is a correlation between the information related to the file of the genetic history of heart disease and heart disease. In the diagnostic process, a patient is considered likely to have a heart disease when the patient has information about a profile of a genetic history of the heart disease.

The incidence relation is defined and updated by authoritative medical staff in the related field or generated by artificial intelligence technology, such as: an intelligent medical brain model; wherein the authoritative medical personnel are, for example, academicians. As shown in fig. 10, it is shown as a representation of the correlation model, in which the correlation between the disease and the disease information is represented by a straight line in the figure. For example: there was a correlation between disease 1 and symptom 2, and no correlation between index 4. In practical application, related diseases can be searched from the correlation model according to symptom signs, abnormal indexes and archive information of a user, and the disease probability of a certain disease is determined according to the correlation model, so that the diagnosis of the disease is completed.

Preferably, in the association model, the disease comprises at least one subtype, the symptom sign comprises at least one attribute, and the examination indicator comprises at least one classification. For example, for the symptom sign of fever, the correlation model includes the attributes of low fever, high fever, etc.; for the body temperature, the correlation model comprises classifications of 36-37 ℃, 37.3-38 ℃, 38.1-40 ℃, more than 40 ℃ and the like. The subtype refers to a combination of symptom signs, examination index, familial inheritance, disease history, medication history, age and/or gender, etc., which can be used to determine the disease type, for example: fever greater than 40 ℃ plus white blood cell count greater than 1000 can be considered one subtype, fever greater than 37 ℃ plus white blood cell count greater than 500 can be considered another subtype. By subdividing the diseases, the symptom signs and the examination indexes in the association model, the embodiment can guide a user (especially a user lacking medical knowledge) to provide more detailed self-health information, and obtain an accurate health self-test result according to the more detailed self-health information.

The self-test template updating module comprises: the related disease searching unit is connected with the health information receiving module and used for searching related diseases in the correlation model according to the health information of the user; and the self-testing template updating unit is connected with the related disease searching unit and the self-testing template generating module and is used for acquiring the disease information of the related diseases according to the correlation model and further updating the self-testing template. Wherein the disease information comprises the symptom signs, examination indexes and/or profile related information of the related diseases. The symptom sign corresponds to symptom sub-information of the user, the check index corresponds to index sub-information of the user, and the file related information corresponds to file sub-information of the user. For example, the symptom sub-information of the user may include fever sub-information, which corresponds to the symptom sign of fever of a disease; the index sub-information of the user includes blood pressure high sub-information which corresponds to an abnormal index of high blood pressure of a disease; the user profile includes cardiac disease history information, which corresponds to the profile-related information of the cardiac disease history of the disease.

In this embodiment, the relevant disease searching unit searches for the relevant disease in the relevant model according to the symptom sub-information and/or the index sub-information; and then, the self-testing template updating unit updates the symptom self-testing sub-template and/or the index self-testing sub-template according to the association model so as to prompt a user to input corresponding health information of the related diseases.

For example, when the symptom sub-information includes a check index 3, the related disease searching unit searches related diseases as disease 1 and disease 3 according to the check index 3, and then the self-testing template updating unit updates the symptom sub-template to prompt the user to input symptom sign 1, symptom sign 2, symptom sign 4, symptom sign 5, and/or symptom sign 6 according to the association model, and updates the index sub-template to prompt the user to input check index 2, check index 3, and/or check index 4. Next, if the user inputs symptom sign 6, the related disease searching unit searches that the related disease is a disease 3 according to the association model, and then the self-testing template updating unit updates the symptom sub-template to prompt the user to input symptom sign 1 and/or symptom sign 4, and updates the index sub-template to prompt the user to input examination index 4.

In this embodiment, the related disease search unit may search for the related disease according to the symptom sub-information and/or the index sub-information input by the user; the self-testing template updating unit can update the self-testing template according to the related diseases, so that the prompting range of the self-testing template for the symptom sub-information and/or the index sub-information is reduced to the symptom sign and/or the examination index of the related diseases, and the user can provide the symptom sub-information and/or the index sub-information in a targeted manner. In the application process, the user can be prompted to provide corresponding health information step by step and in a targeted manner by repeating the above process, and further suspected diseases of the user can be quickly located.

In an embodiment of the present invention, the self-test template includes a symptom self-test sub-template; the symptom self-testing sub-template comprises a symptom prompt label; and the self-testing template updating unit updates the number and/or the type of the symptom prompt tags according to the symptom signs of the related diseases in the association model.

Specifically, in the self-test template generated by the self-test template generation module, the number and the type of the symptom prompting labels are determined according to the health record of the user, the historical self-test report, the current disease information and the like. The health record of the user comprises record sub-information such as sex, age, residence, disease history, genetic history, allergy history and medication history of the user, and the current disease information comprises external information such as season, temperature, humidity and infectious disease outbreak condition. For example, if the health profile of the user shows that the user has a history of hypertension, the symptom prompt tag may include a symptom sign related to hypertension in the self-testing template generated by the self-testing template generating module; if the current flu in the area where the user is located is serious, the symptom prompt tag can be a symptom sign related to the flu in the self-testing template generated by the self-testing template generating module.

And after the user inputs symptom sub-information and/or index sub-information, the self-testing template updating unit updates the number of the symptom prompt tags according to the number of the symptom signs of the related diseases, and updates the types of the prompt tags according to the types of the symptom signs of the related diseases.

In an embodiment of the present invention, the self-testing template further includes an index self-testing sub-template; the index self-testing sub-template comprises an index prompt label; and the self-testing template updating unit updates the number and/or the type of the index prompt tags according to the examination indexes of the relevant diseases in the correlation model.

Specifically, the self-test template generated by the self-test template generation module may not include the index prompt tags, and the number and/or the type of the index prompt tags may also be determined according to the health profile of the user, the historical self-test report, the current disease information, and the like. In this embodiment, the user is prompted to input the corresponding check index in a mode of the index prompt tag, and the index prompt tag allows the user to input the corresponding check index in a click mode, so that standardization of the index sub-information can be guaranteed, the input process of the user can be simplified, and user experience is improved.

In an embodiment of the present invention, the self-test template further includes an archive information sub-template; the file information sub-template comprises a file prompt label; and the self-testing template updating unit updates the number and/or the type of the file prompt tags according to the file related information of the related diseases. Wherein the file sub-information is determined by the health record of the user, which includes but is not limited to the sex, age, residence, disease history, genetic history, allergy history, medication history, etc. of the user; the archive prompt tag comprises a first archive sub-information tag, a second archive sub-information tag and/or an archive input text box.

When at least one item of the file sub-information exists in the health files of the user, the self-testing template generation module reads all the file sub-information of the user from the health files of the user and adds the file sub-information to the file information sub-template to form a first file sub-information label, and the first file sub-information label cannot be edited. In addition, the profile information sub-template may further include a second profile sub-information tag and a profile input text box for prompting a user to input other profile sub-information. The second file sub-information tag allows a user to input corresponding file sub-information in a clicking mode, and the file input text box allows the user to input corresponding file sub-information in a text mode.

When the user does not have the health file or the file sub-information does not exist in the health file of the user, the file information sub-template only comprises a second file sub-information tag and/or a file input text box, and is used for prompting the user to input the file sub-information.

In this embodiment, the association model updating unit can update the association model according to the disease, symptom and/or examination index information of the user, so that the association model can be continuously improved in practical application to ensure the accuracy and integrity of the association model, and the accuracy of health self-test is favorably improved.

In an embodiment of the invention, the self-test report further includes suspected diseases of the user and a probability of suffering from each suspected disease. Specifically, referring to fig. 11, the method for calculating the probability of any suspected disease i by the self-test report generation module includes:

s11, obtaining the diagnosis standard C of the suspected disease i_iAnd according to said diagnostic criterion C_iAnd determining all related sub-information of the suspected disease i. Wherein the diagnostic criteria C_iIs defined as: all sub-information related to the suspected disease i. The sub-information related to the suspected disease i does not necessarily belong to the health information of the user, and the category of the sub-information includes symptom sub-information, index sub-information and/or profile sub-information. The diagnosis standard of each suspected disease is defined by authoritative medical staff in related fields, and if the health information of a certain user contains the diagnosis standard C_iThe probability that the user has the suspected disease i can be regarded as 100%.

S12, acquiring a sub-information collection D in the health information of the user, wherein the sub-information collection D is a collection formed by all sub-information in the health information of the user.

S13, obtaining the diagnosis standard C_iAnd the intersection Q of the sub information collection D, and calculating the disease probability P of the suspected disease i according to the sub information in the intersection Q_i. In particular, the amount of the solvent to be used,

representing the sum of the weights of all sub-information contained in the intersection Q; wherein, W_jRepresents the weight of the sub information j, and

wherein M is a positive integer representing the diagnostic criterion C_iThe number of related sub information in the system is less than or equal to j; n is a radical of_mRepresenting the number of diseases associated with the sub-information m, N_jRepresenting the number of diseases associated with the sub information j.

The above-mentioned prevalence probability calculation process will be described with a specific example based on the correlation model. The diagnosis criteria C of disease 1 are known from the correlation model₁The method comprises the following steps: respectively associated with symptoms1. Symptom subinformation 1, symptom subinformation 2, and symptom subinformation 5 corresponding to symptom 2 and symptom 5; index sub information 1, index sub information 2, and index sub information 3 corresponding to index 1, index 2, and index 3, respectively; archive sub information 3 corresponding to the archive information 3. At this time, M is 7.

According to the correlation model, the number N of diseases associated with symptom sub-information 1 (named as sub-information 1) is known₁Number of diseases N associated with symptom information 2 (named as child information 2) 3₂The number of diseases associated with symptom sub-information 5 (named sub-information 3) is N as 1₃Number of diseases N associated with index subinformation 1 (named subinformation 4) 1₄Number of diseases N associated with sub-information 2 (named sub-information 5) is designated 2₅Number of diseases N associated with index sub-information 3 (named sub-information 6) is 1₆Number of diseases N associated with archive sub-information 3 (named sub-information 7) ═ 3₇＝2。

If the sub-information collection D in the health information of the user comprises: symptom sub-information 1, symptom sub-information 2, symptom sub-information 6, and index sub-information 3. At this time, the diagnostic criteria C_iAnd the intersection Q of the sub-information collection D comprises: symptom sub information 1 (sub information 1), symptom sub information 2 (sub information 2), and index sub information 3 (sub information 6). For Q, it contains sub information 1, sub information 2, and sub information 3, and:

the weight of the sub information 1 is:

the weight of the sub information 2 is:

the weight of the sub information 6 is:

thus, the probability P that the user has disease 1₁＝W₁+W₂+W₃＝35.6％。

In addition, in this embodiment, for the health information of the same user, the disease probability of multiple diseases can be obtained in the manner described in S11 to S13, and one disease with the highest disease probability is selected as the disease that the user is most likely to suffer from.

In an embodiment of the invention, the self-test report includes suspected diseases of the user and a probability of the suspected diseases. The method for calculating the probability of each suspected disease in this embodiment includes:

s21, obtaining a training sample, and training a neural network model by using the training sample to obtain an AI diagnosis model (namely the intelligent medical brain model); preferably, the training sample comprises diagnostic criteria and actual diagnostic cases defined by authoritative medical personnel.

S22, obtaining sub information included in the health information of the user, and processing the sub information by using the AI diagnosis model (i.e., the intelligent medical brain model) to obtain the probability of the suspected diseases.

In the health self-testing system, the self-testing template generating module can generate a self-testing template, and the self-testing template updating module can update the self-testing template according to the health information which is input by a user. The user only needs to input corresponding health information according to the prompt of the self-testing template, and does not need to select the target disease to be tested according to the self condition. Therefore, the intelligent medical service system has the health self-testing function, the health self-testing system can solve the technical problem that a user needs to select a target disease to be tested according to the self condition in the existing scheme, the health self-testing is rapidly and conveniently realized by the user, meanwhile, the earlier-stage work of a doctor is shared, and medical resources are saved.

In conclusion, the present invention effectively overcomes various disadvantages of the prior art and has high industrial utilization value.

The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be accomplished by those skilled in the art without departing from the spirit and scope of the present invention as set forth in the appended claims.

Claims

1. An intelligent medical service system, comprising:

the user side is used for inputting health information related to the user;

the intelligent medical server is in communication connection with the user side and comprises an intelligent medical brain model; the intelligent medical brain model carries out health assessment on the user based on the health information related to the user to obtain a health assessment result;

and the doctor end is in communication connection with the intelligent medical server end and is used for inputting doctor suggestion information based on the health assessment result and feeding back the doctor suggestion information to the user end.

2. The intelligent medical service system according to claim 1, further comprising:

the doctor management terminal is used for inputting a doctor management strategy, a doctor evaluation strategy, a doctor communication learning strategy or/and a doctor cultivation strategy;

the intelligent medical service terminal is in communication connection with the doctor management terminal, a doctor management subsystem is established according to the doctor management strategy, a doctor evaluation subsystem is established according to the doctor evaluation strategy, a doctor communication learning subsystem is established according to the doctor communication learning strategy, or/and a doctor cultivation subsystem is established according to the doctor cultivation strategy;

the doctor end is in communication connection with the intelligent medical service end and is used for participating in the doctor management subsystem, the doctor evaluation subsystem, the doctor communication learning subsystem or/and the doctor cultivation subsystem.

3. The intelligent medical service system according to claim 1, further comprising:

the doctor end issues a query case based on a query model;

and the doctor management subsystem in the intelligent medical service end searches matched doctor ends for solving the inquiry cases based on an inquiry matching strategy.

4. The intelligent medical service system according to claim 1, further comprising:

the user side issues a health self-test request to the intelligent medical service side;

and the intelligent medical brain model in the intelligent medical server performs health self-test on the user based on the health information related to the user and feeds back the health self-test result to the user.

5. The intelligent medical service system according to claim 1, wherein the intelligent medical service terminal comprises a building system of the intelligent medical brain model, the building system comprising:

the neuron model setting module is used for setting the type and the attribute of the neuron model; the types of the neuron models comprise disease neuron models and disease evaluation factor neuron models;

the neuron model generating module is connected with the neuron model setting module and used for generating a corresponding neuron model according to the type and the attribute of the neuron model;

the disease coping standard setting module is used for setting standard information for coping with various diseases;

and the neuron model association module is respectively connected with the neuron model generation module and the disease response standard setting module, and establishes association relations between the disease neuron models and the disease evaluation factor neuron models according to the disease response standard information.

6. The intelligent medical services system of claim 5, wherein the neuron model correlation module comprises:

the first association unit is used for initially and custom-establishing association relations between the neuron models of various diseases and the neuron models of various disease evaluation factors according to preset disease response standard information; or/and

a second association unit for updating and establishing association W between each disease neuron model and each disease evaluation factor neuron model according to preset disease response standard information_j；

Wherein, W_jA correlation coefficient representing disease evaluation factor data j in the disease cause subset and corresponding disease type data; m is a positive integer representing the number of disease evaluation factor data in the disease cause subset, N_mNumber of disease type data, N, associated with disease evaluation factor data m_jRepresents the number of disease type data associated with the disease evaluation factor data j, and j ≦ M.

7. The intelligent medical service system according to claim 5, wherein the system for building an intelligent medical brain model further comprises:

and the disease response standard updating module is in communication connection with the disease response standard setting module and is used for adding, deleting or/and updating the disease response standard information.

8. The intelligent medical services system of claim 7, wherein the neuron model correlation module further comprises:

a third correlation unit for updating and adjusting the correlation W between each disease neuron model and each disease evaluation factor neuron model according to the increased, deleted or/and updated disease coping standard information_j。

9. The intelligent medical services system of claim 5, wherein the neuron model generation module comprises:

the neuron model input generation unit is used for generating a corresponding neuron model input function according to the type and the attribute of the neuron model;

the neuron model processing and generating unit is used for generating a corresponding processing function of the neuron model according to the type and the attribute of the neuron model;

and the neuron model output generation unit is used for generating a corresponding neuron model output function according to the type and the attribute of the neuron model.

10. The intelligent medical services system of claim 9, wherein: when the neuron model generation module generates the disease neuron model,

the neuron model input generation unit generates a corresponding disease neuron model input function according to the attribute of the disease neuron model; the attributes of the disease neuron model comprise the large class of diseases, the names of the diseases, the small class of the diseases and disease related information;

the neuron model processing and generating unit generates a processing function of the corresponding disease neuron model according to the attribute of the disease neuron model;

and the neuron model output generation unit generates a corresponding disease neuron model output function according to the attribute of the disease neuron model.

11. The intelligent medical service system according to claim 9, wherein when the neuron model generation module generates the disease evaluation factor-based neuron model,

the neuron model input generation unit generates a corresponding disease evaluation factor neuron model input function according to the attribute of the disease evaluation factor neuron model; the attributes of the disease evaluation factor neuron model comprise the category of the disease evaluation factor, the name of the disease evaluation factor, the grade of the disease evaluation factor and the related information of the disease evaluation factor;

the neuron model processing generation unit generates a processing function of the corresponding disease evaluation factor neuron model according to the attribute of the disease evaluation factor neuron model;

the neuron model output generation unit generates a corresponding disease evaluation factor neuron model output function according to the attribute of the disease evaluation factor neuron model.

12. The intelligent medical service system according to claim 5, wherein the system for building an intelligent medical brain model further comprises:

the information acquisition module is in communication connection with the disease evaluation factor neuron model and used for acquiring a disease evaluation factor data set and inputting the disease evaluation factor data set into the disease evaluation factor neuron model;

the disease acquisition module is in communication connection with the disease neuron model and acquires an output result of the disease neuron model;

and the analysis processing module is respectively connected with the disease acquisition module and the disease coping standard setting module, compares and analyzes the output result of the disease neuron model and an expected result, and outputs a relevant instruction for judging whether to correct corresponding disease coping standard information.

13. An intelligent medical service method, characterized in that the intelligent medical service method comprises:

acquiring health information related to a user;

utilizing an intelligent medical brain model; the intelligent medical brain model carries out health assessment on the user based on the health information related to the user to obtain a health assessment result;

and acquiring doctor suggestion information based on the health assessment result and feeding back the doctor suggestion information to the user.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: the computer program when executed by a processor implementing the method as claimed in any one or several of the steps of claim 13.