A method and system for online intelligent medical services

Abstract

The present invention discloses a method for online intelligent medical services. The method includes: obtaining symptom description information of a first user; inputting the symptom description information of the first user into an offline examination judgment model to determine whether the first user needs to undergo an offline examination; if an offline examination is required, obtaining the first location of the first user; obtaining a first doctor sub-database based on the first location; extracting features from the symptom description information of the first user to obtain first keyword information; obtaining first doctor information from the first doctor sub-database according to the first keyword information; obtaining a first examination prescription according to the symptom description information; obtaining first examination appointment information according to the first examination prescription; obtaining a first examination result; obtaining first diagnosis and treatment information according to the first examination result and the symptom description information; and obtaining authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information.

Description

Technical Field

[0001] The present invention relates to the field of medical services, and in particular, to a smart medical online service method and system. Background Art

[0002] Smart healthcare is a proprietary medical term that has emerged in recent years. By building a regional healthcare information platform for health records and using the most advanced Internet of Things technology, it realizes the interaction between patients, medical staff, medical institutions, and medical devices, and gradually achieves informatization. Due to the imperfection of the domestic public medical management system, problems such as high medical costs, few channels, and low coverage rate trouble the general public. By establishing a smart medical information network platform system, patients can enjoy safe, convenient, and high-quality medical services with a shorter waiting time and by paying basic medical expenses.

[0003] In the process of implementing the inventive technical solution in the embodiments of the present application, the inventors found that the above technologies have at least the following technical problems:

[0004] Existing smart medical services focus more on the online diagnosis and recording processes and do not pay attention to the interaction between online and offline. This causes an information gap between the online and offline diagnosis and treatment processes, is not conducive to the synchronous progress of the online and offline diagnosis and treatment processes, and is prone to misdiagnosis and other situations, affecting the diagnosis and treatment process of patients. Summary of the Invention

[0005] By providing a smart medical online service method and system in the embodiments of the present application, the technical problem in the prior art that smart medical services cannot meet the synchronous interaction between online and offline, resulting in an information gap between the online and offline diagnosis and treatment processes, is solved. The purpose of diagnosing patients by combining online and offline diagnosis information is achieved, the technical effect of improving the integration degree of online and offline diagnosis and treatment information is realized, and the diagnosis and treatment efficiency is further improved.

[0006] In view of the above problems, the embodiments of the present application provide a smart medical online service method and system.

[0007] In a first aspect, the present application provides a method for intelligent medical online services. Among them, the method includes: obtaining symptom description information of a first user; inputting the symptom description information of the first user into an offline examination judgment model to judge whether the first user needs to undergo an offline examination; if the first user needs to undergo an offline examination, obtaining the first location of the first user; obtaining a first doctor sub-database according to the first location, the first doctor sub-database includes doctor information within a first block, where the first location is within the first block; extracting features from the symptom description information of the first user to obtain first keyword information; obtaining first doctor information from the first doctor sub-database according to the first keyword information; obtaining a first examination prescription according to the symptom description information, the first examination prescription is an examination prescription issued by the first doctor; obtaining first examination appointment information according to the first examination prescription, the first examination appointment information is the examination appointment information of the first user at the hospital where the first doctor is located; obtaining a first examination result; obtaining first diagnosis and treatment information according to the first examination result and the symptom description information; obtaining authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information.

[0008] On the other hand, the present application also provides a smart medical online service system, wherein the system includes: a first acquisition unit configured to acquire symptom description information of a first user; a first judgment unit configured to input the symptom description information of the first user into an offline examination judgment model to judge whether the first user needs to undergo an offline examination; a second acquisition unit configured to acquire a first location of the first user when the first user needs to undergo an offline examination; a third acquisition unit configured to acquire a first doctor sub-database according to the first location, the first doctor sub-database including doctor information within a first block, wherein the first location is within the first block; a fourth acquisition unit configured to perform feature extraction on the symptom description information of the first user to acquire first keyword information; a fifth acquisition unit configured to acquire first doctor information from the first doctor sub-database according to the first keyword information; a sixth acquisition unit configured to acquire a first examination prescription according to the symptom description information, the first examination prescription being an examination prescription issued by the first doctor; a seventh acquisition unit configured to acquire first examination reservation information according to the first examination prescription, the first examination reservation information being examination reservation information of the first user at the hospital where the first doctor is located; an eighth acquisition unit configured to acquire a first examination result; a ninth acquisition unit configured to acquire first diagnosis and treatment information according to the first examination result and the symptom description information; a tenth acquisition unit configured to acquire authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information.

[0009] On the other hand, an embodiment of the present application also provides a smart medical online service system, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method described in the first aspect above are implemented.

[0010] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0011] The present application provides a method for intelligent medical online services, which includes obtaining symptom description information of a first user; inputting the symptom description information of the first user into an offline examination judgment model to determine whether the first user needs to undergo an offline examination; if the first user needs to undergo an offline examination, obtaining the first location of the first user; obtaining a first doctor sub-database according to the first location, where the first doctor sub-database includes doctor information within a first block, and the first location is within the first block; extracting features from the symptom description information of the first user to obtain first keyword information; obtaining first doctor information from the first doctor sub-database according to the first keyword information; obtaining a first examination prescription according to the symptom description information, where the first examination prescription is an examination prescription issued by the first doctor; obtaining first examination appointment information according to the first examination prescription, where the first examination appointment information is examination appointment information of the first user at the hospital where the first doctor is located; obtaining a first examination result; obtaining first diagnosis and treatment information according to the first examination result and the symptom description information; obtaining authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information, solving the technical problem in the prior art that intelligent medical services cannot meet the synchronous interaction between online and offline, resulting in an information gap in the online and offline diagnosis and treatment processes, achieving the purpose of diagnosing and treating patients by combining online and offline diagnosis information, realizing the technical effect of improving the integration degree of online and offline diagnosis and treatment information, and further improving the diagnosis and treatment efficiency.

[0012] The above description is an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically illustrates the specific embodiments of the present application. Brief Description of the Drawings

[0013] Figure 1 It is a schematic flowchart of a method for intelligent medical online services according to an embodiment of the present application;

[0014] Figure 2 It is a schematic structural diagram of an intelligent medical online service system according to an embodiment of the present application;

[0015] Figure 3 It is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.

[0016] Explanation of the accompanying drawings: first obtaining unit 11, first judging unit 12, second obtaining unit 13, third obtaining unit 14, fourth obtaining unit 15, fifth obtaining unit 16, sixth obtaining unit 17, seventh obtaining unit 18, eighth obtaining unit 19, ninth obtaining unit 20, tenth obtaining unit 21, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. DETAILED DESCRIPTION

[0017] The present application provides a smart medical online service method, which obtains symptom description information of a first user, performs an offline examination on the first user, and obtains first diagnosis and treatment information output by a diagnosis and treatment result prediction model based on the first examination result and the symptom description information, obtains the first doctor's authentication information for the first diagnosis and treatment information based on the first diagnosis and treatment information, and the doctor reconfirms the result, thereby improving the efficiency and accuracy of online diagnosis, and solves the technical problem in the prior art that smart medical services cannot meet the requirements of online and offline synchronous interaction, resulting in information gaps in the online and offline diagnosis and treatment processes, and achieves the purpose of combining online and offline diagnostic information to diagnose and treat patients, and realizes the technical effect of improving the integration of online and offline diagnosis and treatment information, and further improves the diagnosis and treatment efficiency.

[0018] Below, example embodiments of the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein.

[0019] Application Overview

[0020] By establishing a smart medical information network platform system, patients can enjoy safe, convenient and high-quality diagnosis and treatment services with a shorter waiting time and by paying basic medical expenses. As existing smart medical services focus more on online diagnosis and treatment and recording processes, and do not pay attention to online and offline interactions, there is an information gap between online and offline diagnosis and treatment processes, which is not conducive to the synchronization of online and offline diagnosis and treatment processes, and is prone to misdiagnosis, affecting the diagnosis and treatment process of patients.

[0021] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0022] The present application provides a method for online intelligent medical services. The method includes: obtaining symptom description information of a first user; inputting the symptom description information of the first user into an offline examination judgment model to determine whether the first user needs to undergo an offline examination; if the first user needs to undergo an offline examination, obtaining a first location of the first user; obtaining a first doctor sub-database according to the first location, where the first doctor sub-database includes doctor information within a first block, and the first location is within the first block; extracting features from the symptom description information of the first user to obtain first keyword information; obtaining first doctor information from the first doctor sub-database according to the first keyword information; obtaining a first examination prescription according to the symptom description information, where the first examination prescription is an examination prescription issued by the first doctor; obtaining first examination appointment information according to the first examination prescription, where the first examination appointment information is examination appointment information of the first user at the hospital where the first doctor is located; obtaining a first examination result; obtaining first diagnosis and treatment information according to the first examination result and the symptom description information; and obtaining authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information.

[0023] After introducing the basic principle of the present application, various non-limiting embodiments of the present application will be specifically introduced below with reference to the accompanying drawings of the specification.

[0024] Example 1

[0025] As Figure 1 shown, an embodiment of the present application provides a method for online intelligent medical services. The method includes:

[0026] Step S100: Obtain symptom description information of a first user;

[0027] Specifically, the symptom description information of the first user is comprehensive information expressing the physical condition and specific disease symptoms of the first user, which is actively input by the first user into a user terminal. The user terminal is a software terminal supporting intelligent medical services, and the software terminal can be a computer or mobile phone software. Through the active input of the first user, information accurately describing the user's physical condition can be obtained, laying an information foundation for subsequent "symptom-based diagnosis and treatment".

[0028] Step S200: Input the symptom description information of the first user into an offline examination judgment model to determine whether the first user needs to undergo an offline examination;

[0029] Step S300: If the first user needs to undergo an offline examination, obtain a first location of the first user;

[0030] Specifically, the offline examination judgment model is a formal expression obtained by abstracting the symptom description information of the first user, and consists of three parts: objectives, variables, and relationships. By inputting the symptom description information of the first user into the offline examination judgment model, it can be determined whether the first user needs an offline examination. When the judgment result is that the first user needs an offline examination, the first location of the first user can be obtained. The first location includes, but is not limited to, the geographical location information of the first user and the regional location information divided according to the medical resource situation. Obtaining the first location of the first user can match a suitable offline diagnosis and treatment plan for the first user, making preparations for realizing the integration of online and offline information.

[0031] Step S400: Obtain a first doctor sub-database according to the first location. The first doctor sub-database includes doctor information within the first block, where the first location is within the first block;

[0032] Specifically, obtaining the first doctor sub-database according to the first location. The first doctor sub-database is a set of doctors matched for the first user, and this set includes all doctors in the adjacent range with the main treatment categories. The adjacent range is the first block. Regardless of the symptoms of the first user, a doctor with a matching specialty can be found within the first block. Further, the first location is a lower-level unit of the first block. After obtaining the first location information of the first user, all doctor information in the first block is extracted to find the doctor who best matches the symptom description information of the first user within the adjacent range, providing a more convenient and accurate diagnosis and treatment for the first user.

[0033] Step S500: Extract features from the symptom description information of the first user to obtain first keyword information;

[0034] Step S600: Obtain first doctor information from the first doctor sub-database according to the first keyword information;

[0035] Specifically, the symptom description information of the first user is a comprehensive and detailed description, which is obtained by the active input of the first user. To facilitate retrieval, the symptom description information of the first user is filtered and screened to extract useful feature information, thereby obtaining the first keyword information. The first keyword information is the result of information extraction of the symptom description information of the first user. According to the first keyword information, the first doctor information is obtained from the first doctor sub-database. The first doctor information is the information of the medical staff who provides diagnosis and treatment for the first user. After the first user receives online diagnosis and treatment from the first doctor, he or she receives treatment in an offline hospital, which can greatly improve the hospital's diagnosis and treatment efficiency and effectively alleviate the problem of cumbersome and time-consuming diagnosis and treatment procedures.

[0036] Step S700: obtaining a first examination prescription according to the symptom description information, where the first examination prescription is an examination prescription issued by the first doctor;

[0037] Step S800: obtaining first examination appointment information according to the first examination prescription, where the first examination appointment information is the examination appointment information of the first user at the hospital where the first doctor is located;

[0038] Specifically, the first examination prescription is an examination prescription issued by the first doctor for the first user based on the symptom description information. The content of the prescription can be routine examinations that the first user should undergo, such as blood routine, urine routine, liver and kidney function tests, etc., or imaging examinations that the first user should undergo. According to the first examination prescription, the first examination appointment information is obtained. The first examination appointment information is the examination appointment information of the first user in the hospital where the first doctor is located. The first examination appointment information includes but is not limited to the appointment time, examination items and person in charge of the examination. Through the first examination appointment information, the details of the diagnosis and treatment of the first user in the hospital where the first doctor is located can be obtained, which provides a basis for judging the rationality of the diagnosis and treatment results.

[0039] Step S900: obtaining a first inspection result;

[0040] Step S1000: obtaining first diagnosis and treatment information according to the first examination result and the symptom description information;

[0041] Specifically, the first examination result is an examination report obtained by the first user after the examination at the hospital where the first doctor is located. The first diagnosis and treatment information is obtained based on the first examination result and the symptom description information. The first diagnosis and treatment information includes the diagnosis result of the first user, the treatment to be performed, and the physical condition information after the treatment. Obtaining the first diagnosis and treatment information provides a basis for judging the rationality of the diagnosis and treatment results.

[0042] Step S1100: Obtain the authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information.

[0043] Specifically, the authentication information is the diagnosis and treatment conclusion obtained by the first doctor through reconfirmation of the first diagnosis and treatment information based on the actual treatment situation of the patient. The first user undergoes an online diagnosis, follows the examination prescription issued by the first doctor, determines the treatment plan through examinations, and conducts the final treatment. The first doctor analyzes whether the online diagnosis reaches the expected result by comparing the first diagnosis and treatment information of the first user with the actual treatment result, so as to achieve the purpose of improving the efficiency and accuracy of online diagnosis.

[0044] Further, step S200 of the embodiment of the present application further includes:

[0045] Step S210: Obtain the first symptom information and the second symptom information according to the symptom description information of the first user;

[0046] Step S220: Use the first symptom information as the abscissa;

[0047] Step S230: Use the second symptom information as the ordinate;

[0048] Step S240: Construct the offline examination judgment model based on the first rectangular coordinate system of the abscissa and the ordinate, wherein the offline examination judgment model includes a first non-linear regression line;

[0049] Step S250: Input the first symptom information and the second symptom information into the offline examination judgment model. One side of the first non-linear regression line represents the first result, and the other side represents the second result. The first result indicates that the first user needs to undergo an offline examination, and the second result indicates that the first user does not need to undergo an offline examination.

[0050] Specifically, the offline examination and judgment model is a non-linear regression model based on a rectangular coordinate system. When the dependent variable of the regression model is a function form of the independent variable above the first order, and the regression law is manifested as various curves with different shapes on the graph, it is called non-linear regression. Such models are called non-linear regression models. In many practical problems, the regression function is often a relatively complex non-linear function. The solution of non-linear functions can generally be divided into two categories: those that can be transformed into linear and those that cannot be transformed into linear. The basic method for dealing with non-linear regression that can be linearized is to transform the non-linear regression into linear regression through variable transformation and then use the linear regression method to process. Assume that according to theory or experience, a non-linear expression between the output variable and the input variable has been obtained, but the coefficients of the expression are unknown, and the values of the coefficients need to be determined based on n observations of the input and output. The coefficient values are obtained according to the least squares principle, and the resulting model is a non-linear regression model. By inputting the first symptom information and the second symptom information into the offline examination and judgment model, it is possible to approximately obtain that the offline examination and judgment result falls on one side of the first non-linear regression line, and finally obtain the conclusion of whether the first user needs to undergo an offline examination.

[0051] Further, the embodiment of the present application further includes step S1200, where step S1200 includes:

[0052] Step S1210: Obtain the age information of the first user;

[0053] Step S1220: Obtain the gender information of the first user;

[0054] Step S1230: Obtain a first age parameter according to the age information and the gender information of the first user;

[0055] Step S1240: Optimize the offline examination and judgment model according to the first age parameter to obtain an optimized offline examination and judgment model.

[0056] Specifically, different patients have different constitutions, and there are also certain differences in treatment methods and detection means. For example, when undergoing drug treatment, the drug dosage for children is only half of that for adults. By obtaining the age and gender information of the first user, a first age parameter is obtained. The first age parameter is the influencing factor of the age and gender of the first user on the diagnosis and treatment process. The offline examination and judgment model is optimized according to the first age parameter to obtain an optimized offline examination and judgment model. The optimized offline examination and judgment model is the result of optimizing and adjusting the offline examination and judgment model by taking the age and gender information of the first user as the influencing factors in the diagnosis and treatment process, taking into account the professionalism of the mathematical model and the uniqueness of different users, and further improving the accuracy of the offline examination result judgment.

[0057] Furthermore, the embodiment of the present application further includes an optimized offline inspection judgment model, where the optimized offline inspection judgment model is:

[0058]

[0059] where a is the first age parameter and b is a constant;

[0060] x is the first symptom information;

[0061] y is the second symptom information.

[0062] Specifically, the optimized offline inspection judgment model is a linearized non - linear regression model. Taking the first age parameter as a coefficient, introducing a constant, and forming a function model with the first symptom information and the second symptom information, the output result of which is used to judge whether the first user needs to undergo offline inspection. The optimized offline inspection judgment model improves the judgment accuracy and provides a basis for improving the integration of online and offline information.

[0063] Furthermore, step S800 of the embodiment of the present application further includes:

[0064] Step S810: Obtain the first inspection item information according to the first inspection prescription;

[0065] Step S820: Obtain the time arrangement information of the first doctor;

[0066] Step S830: Determine the first inspection appointment information according to the first inspection item information and the time arrangement information of the first doctor.

[0067] Specifically, since there are usually many patients visiting the hospital, the reasonable arrangement of the doctor's time is particularly important. According to the first inspection prescription, the first inspection item information is obtained, and the first inspection item information is all the physical examination items that the first user needs to undergo. The time arrangement information of the first doctor is the schedule obtained from the time arrangement for the patients diagnosed by the first doctor. Through the time arrangement information, the working conditions of the first doctor within a period of time can be obtained, so as to reasonably arrange the diagnosis and treatment time of the first user according to this schedule. According to the first inspection item information and the time arrangement information of the first doctor, the first inspection appointment information is determined, and the first inspection appointment information is the most reasonable exact appointment information for the first user to undergo inspection obtained through the overall planning of the first inspection item information and the time arrangement information of the first doctor. By determining the first inspection appointment information, the patient's appointment time can be scientifically and reasonably arranged to maximize efficiency.

[0068] Further, step S400 of the embodiment of the present application further includes:

[0069] Step S410: Obtain a first region;

[0070] Step S420: Construct a doctor database corresponding to the first region according to the first region;

[0071] Step S430: Obtain the permanent population information of the first region;

[0072] Step S440: Divide the first region into blocks according to the population density of the first region to obtain first block division information;

[0073] Step S450: Divide the doctor database into sub-databases according to the first block division information to obtain multiple doctor sub-databases, where the first doctor sub-database corresponds to the first block.

[0074] Specifically, due to the different population densities in each region, the distribution of medical resources is unbalanced, which greatly reduces the medical treatment efficiency of residents in some regions. Therefore, by obtaining the permanent population information of the first region, dividing the first region into blocks according to the population density to obtain first block division information, and redistributing the per capita medical resource situation in the first region, the purpose of resource balance is achieved. The first block division information is the partition situation information rationally divided according to the population density. According to the first block division information, the doctor database is divided into sub-databases to obtain multiple doctor sub-databases. After the first region is rationally divided according to the population density, and then the doctor database is divided into sub-databases according to the situation of the doctor database, the first doctor sub-database corresponds to the first block, achieving the purpose of reasonable distribution of medical resources and further improving the medical treatment efficiency of residents.

[0075] Further, the embodiment of the present application further includes step S1300, where step S1300 includes:

[0076] Step S1310: Input the first examination result and the symptom description information as input information into a diagnosis and treatment result prediction model;

[0077] Step S1320: The diagnosis and treatment result prediction model is trained to convergence through multiple groups of training data. Each group of data in the multiple groups of training data includes the first examination result, the symptom description information, and identification information for identifying the first diagnosis and treatment information;

[0078] Step S1330: Obtain the output information of the diagnosis and treatment result prediction model, where the output information includes the first diagnosis and treatment information.

[0079] Specifically, the diagnosis and treatment result prediction model is a model that conducts targeted analysis based on the patient's visit information and is further trained based on the diagnosis and treatment information and results. Therefore, the first examination result and the symptom description information are used as input information to be input into the diagnosis and treatment result prediction model for data analysis, thereby obtaining output information, which includes the first diagnosis and treatment information. In detail, the first diagnosis and treatment information is the prediction information of the diagnosis result of the first user. Among them, the diagnosis and treatment result prediction model is a model established based on a neural network model. A neural network is an operation model composed of a large number of neurons connected to each other, and the output of the network is expressed according to a logical strategy of the connection method of the network, achieving the technical effect of predicting the diagnosis and treatment result through the model and improving the integration degree of online and offline intelligent medical care.

[0080] Furthermore, the first examination result and the symptom description information are used as input information to be input into the diagnosis and treatment result prediction model to predict the diagnosis and treatment result of the first user. The diagnosis and treatment result prediction model is trained by building a model based on the neural network model. Further, the training process is essentially a supervised learning process. Each set of supervised data includes the first examination result, the symptom description information, and the identification information used to identify the first diagnosis and treatment information. The neural network model continuously self-corrects and adjusts until the obtained output result is consistent with the identification information, ending the supervised learning of this group of data and proceeding to the supervised learning of the next group of data. When the output information of the remote management solution formulation model reaches a predetermined accuracy rate / reaches a convergence state, the supervised learning process ends. By continuously training multiple sets of data, the technical effect of outputting accurate first diagnosis and treatment information is achieved.

[0081] In summary, the intelligent medical online service method provided by the embodiments of the present application has the following technical effects:

[0082] 1. This application provides a smart medical online service method, which includes obtaining the symptom description information of the first user; inputting the symptom description information of the first user into an offline examination judgment model to determine whether the first user needs to undergo an offline examination; if the first user needs to undergo an offline examination, obtaining the first location of the first user; obtaining a first doctor sub-database according to the first location, where the first doctor sub-database includes doctor information within a first block, and the first location is within the first block; extracting features from the symptom description information of the first user to obtain first keyword information; obtaining first doctor information from the first doctor sub-database according to the first keyword information; obtaining a first examination prescription according to the symptom description information, where the first examination prescription is an examination prescription issued by the first doctor; obtaining first examination appointment information according to the first examination prescription, where the first examination appointment information is the examination appointment information of the first user at the hospital where the first doctor is located; obtaining a first examination result; obtaining first diagnosis and treatment information according to the first examination result and the symptom description information; obtaining the authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information, solving the technical problem in the prior art that the smart medical service cannot meet the synchronous interaction between online and offline, resulting in an information gap in the online and offline diagnosis and treatment processes, achieving the purpose of diagnosing and treating patients by combining online and offline diagnosis information, realizing the technical effect of improving the integration degree of online and offline diagnosis and treatment information, and further improving the diagnosis and treatment efficiency.

[0083] 2. Input the first examination result and the symptom description information as input information into a diagnosis and treatment result prediction model to obtain the output information of the diagnosis and treatment result prediction model, where the output information includes the first diagnosis and treatment information. The diagnosis and treatment result prediction model is a machine learning model. Based on the way that the machine learning model can continuously learn and acquire experience to process data, the acquisition of the first diagnosis and treatment information result is made more accurate.

[0084] 3. The offline examination judgment model is a non-linear regression model based on a rectangular coordinate system. Input the first symptom information and the second symptom information into the offline examination judgment model, and its output result is used to determine whether the first user needs to undergo an offline examination. The non-linear regression model can explicitly and optimize the constraints of the control quantity and the state quantity, and can intuitively express the prediction result.

[0085] Example 2

[0086] Based on the same inventive concept as a smart medical online service method in the foregoing embodiment, the present invention also provides a smart medical online service system, as Figure 2 shown. The system includes:

[0087] The first acquisition unit 11 is configured to acquire the symptom description information of the first user;

[0088] The first judgment unit 12 is configured to input the symptom description information of the first user into an offline examination judgment model to judge whether the first user needs to undergo an offline examination;

[0089] The second acquisition unit 13 is configured to acquire the first location of the first user when the first user needs to undergo an offline examination;

[0090] The third acquisition unit 14 is configured to acquire a first doctor sub-database according to the first location, where the first doctor sub-database includes doctor information within a first block, and the first location is within the first block;

[0091] The fourth acquisition unit 15 is configured to perform feature extraction on the symptom description information of the first user to obtain first keyword information;

[0092] The fifth acquisition unit 16 is configured to acquire first doctor information from the first doctor sub-database according to the first keyword information;

[0093] The sixth acquisition unit 17 is configured to acquire a first examination prescription according to the symptom description information, where the first examination prescription is an examination prescription issued by the first doctor;

[0094] The seventh acquisition unit 18 is configured to acquire first examination appointment information according to the first examination prescription, where the first examination appointment information is examination appointment information of the first user at the hospital where the first doctor is located;

[0095] The eighth acquisition unit 19 is configured to acquire a first examination result;

[0096] The ninth acquisition unit 20 is configured to acquire first diagnosis and treatment information according to the first examination result and the symptom description information;

[0097] The tenth acquisition unit 21 is configured to acquire the authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information.

[0098] Further, the system further includes:

[0099] The eleventh acquisition unit is configured to acquire first symptom information and second symptom information according to the symptom description information of the first user;

[0100] A first construction unit, which is used to use the first symptom information as the abscissa; use the second symptom information as the ordinate; construct the offline examination judgment model based on a first rectangular coordinate system of the abscissa and the ordinate, wherein a first non-linear regression line is included in the offline examination judgment model;

[0101] A first input unit, which is used to input the first symptom information and the second symptom information into the offline examination judgment model. One side of the first non-linear regression line represents a first result, and the other side of the first non-linear regression line represents a second result. The first result indicates that the first user needs to undergo an offline examination, and the second result indicates that the first user does not need to undergo an offline examination.

[0102] Further, the device further includes:

[0103] A twelfth acquisition unit, which is used to acquire the age information of the first user;

[0104] A thirteenth acquisition unit, which is used to acquire the gender information of the first user;

[0105] A fourteenth acquisition unit, which is used to acquire a first age parameter according to the age information and the gender information of the first user;

[0106] A fifteenth acquisition unit, which is used to optimize the offline examination judgment model according to the first age parameter to obtain an optimized offline examination judgment model.

[0107] Further, the device further includes an optimized offline examination judgment model unit, which is used to judge whether the first user needs to undergo an offline examination:

[0108] Further, the device further includes:

[0109] A sixteenth acquisition unit, which is used to acquire first examination item information according to the first examination prescription;

[0110] A seventeenth acquisition unit, which is used to acquire the time arrangement information of a first doctor;

[0111] A first determination unit, which is used to determine the first examination appointment information according to the first examination item information and the time arrangement information of the first doctor.

[0112] Further, the device further includes:

[0113] The eighteenth acquisition unit is configured to acquire a first region;

[0114] The second construction unit is configured to construct a doctor database corresponding to the first region according to the first region;

[0115] The nineteenth acquisition unit is configured to acquire the permanent population information of the first region;

[0116] The twentieth acquisition unit is configured to divide the first region into blocks according to the permanent population information of the first region to obtain first block division information;

[0117] The twenty-first acquisition unit is configured to divide the doctor database into sub-databases according to the first block division information to obtain a plurality of doctor sub-databases, wherein the first doctor sub-database corresponds to the first block.

[0118] Further, the apparatus further includes:

[0119] The second input unit is configured to input the first examination result and the symptom description information as input information into a diagnosis and treatment result prediction model; the diagnosis and treatment result prediction model is trained to convergence through multiple groups of training data, and each group of data in the multiple groups of training data includes the first examination result, the symptom description information, and identification information for identifying first diagnosis and treatment information;

[0120] The twenty-second acquisition unit is configured to acquire output information of the diagnosis and treatment result prediction model, and the output information includes the first diagnosis and treatment information.

[0121] The foregoing Figure 1 The intelligent medical online service method and specific examples in Embodiment 1 are equally applicable to the intelligent medical online service system in this embodiment. Through the foregoing detailed description of the intelligent medical online service method, those skilled in the art can clearly know the intelligent medical online service system in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be described in detail herein.

[0122] Exemplary electronic device

[0123] Next, reference is made to Figure 3 to describe the electronic device according to an embodiment of the present application.

[0124] Figure 3 The structural schematic diagram of an electronic device according to an embodiment of the present application is illustrated.

[0125] Based on the inventive concept of a smart healthcare online service method in the foregoing embodiments, the present invention further provides a smart healthcare online service system, on which a computer program is stored, and when the program is executed by a processor, the steps of any of the methods of the smart healthcare online service method described above are implemented.

[0126] Among them, in Figure 3 it, the bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, and bus 300 links various circuits including one or more processors represented by processor 302 and a memory represented by memory 304 together. Bus 300 may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art, and thus will not be further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices on the transmission medium.

[0127] Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0128] The present application provides a smart healthcare online service method, among which, the method is applied to a smart healthcare online service system, and the method includes: obtaining symptom description information of a first user; inputting the symptom description information of the first user into an offline examination judgment model to judge whether the first user needs to undergo an offline examination; if the first user needs to undergo an offline examination, obtaining the first location of the first user; obtaining a first doctor sub-database according to the first location, the first doctor sub-database includes doctor information within a first block, wherein the first location is within the first block; extracting features from the symptom description information of the first user to obtain first keyword information; obtaining first doctor information from the first doctor sub-database according to the first keyword information; obtaining a first examination prescription according to the symptom description information, the first examination prescription is an examination prescription issued by the first doctor; obtaining first examination appointment information according to the first examination prescription, the first examination appointment information is examination appointment information of the first user in the hospital where the first doctor is located; obtaining a first examination result; obtaining first diagnosis and treatment information according to the first examination result and the symptom description information; obtaining authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information.

[0129] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, an apparatus, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0130] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a system for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0131] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction system that realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks. Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0133] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. An online service method for intelligent medical care, wherein, The method is applied to an intelligent medical online service platform, and the method includes: Obtain the symptom description information of the first user; Input the symptom description information of the first user into an offline examination judgment model to judge whether the first user needs to undergo an offline examination; If the first user needs to undergo an offline examination, obtain the first location of the first user; Obtain a first doctor sub-database according to the first location, where the first doctor sub-database includes doctor information within the first block, and the first location is within the first block; Extract features from the symptom description information of the first user to obtain first keyword information; Obtain first doctor information from the first doctor sub-database according to the first keyword information; Obtain a first examination prescription according to the symptom description information, where the first examination prescription is an examination prescription issued by the first doctor; Obtain first examination appointment information according to the first examination prescription, where the first examination appointment information is the examination appointment information of the first user at the hospital where the first doctor is located; Obtain a first examination result; Obtain first diagnosis and treatment information according to the first examination result and the symptom description information; Obtain the authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information; The method further includes: Obtain a first region; Construct a doctor database corresponding to the first region according to the first region; Obtain the permanent population information of the first region; According to the permanent population information of the first region, divide the first region into blocks according to the population density to obtain first block division information; According to the first block division information, divide the doctor database into sub-databases to obtain multiple doctor sub-databases, where the first doctor sub-database corresponds to the first block.

2. The method according to claim 1, wherein The step of inputting the symptom description information of the first user into an offline examination judgment model to judge whether the first user needs to undergo an offline examination includes: Obtain first symptom information and second symptom information according to the symptom description information of the first user; Use the first symptom information as the abscissa; Use the second symptom information as the ordinate; Construct the offline examination judgment model based on the first rectangular coordinate system of the abscissa and the ordinate, where the offline examination judgment model includes a first non-linear regression line; Input the first symptom information and the second symptom information into the offline examination judgment model. One side of the first non-linear regression line represents a first result, and the other side represents a second result. The first result indicates that the first user needs to undergo an offline examination, and the second result indicates that the first user does not need to undergo an offline examination.

3. The method according to claim 2, wherein The method includes: Obtain the age information of the first user; Obtain the gender information of the first user; Obtain a first age parameter according to the age information and the gender information of the first user; Optimize the offline examination judgment model according to the first age parameter to obtain an optimized offline examination judgment model.

4. The method according to claim 3, wherein, The optimized offline examination judgment model is: ， Among them, is the first age parameter, and b is a constant; x is the first symptom information; y is the second symptom information.

5. The method according to claim 1, wherein Obtaining the first examination appointment information according to the first examination prescription includes: Obtaining the first examination item information according to the first examination prescription; Obtaining the schedule information of the first doctor; Determining the first examination appointment information according to the first examination item information and the schedule information of the first doctor.

6. The method according to claim 1, wherein Obtaining the first diagnosis and treatment information according to the first examination result and the symptom description information includes: Using the first examination result and the symptom description information as input information and inputting them into a diagnosis and treatment result prediction model; The diagnosis and treatment result prediction model is trained to convergence through multiple groups of training data. Each group of data in the multiple groups of training data includes the first examination result, the symptom description information, and identification information for identifying the first diagnosis and treatment information; Obtaining the output information of the diagnosis and treatment result prediction model, where the output information includes the first diagnosis and treatment information.

7. A smart healthcare online service system, applied to the method according to any one of claims 1-6, wherein, The system includes: A first obtaining unit configured to obtain the symptom description information of a first user; A first judging unit configured to input the symptom description information of the first user into an offline examination judgment model to judge whether the first user needs to undergo an offline examination; A second obtaining unit configured to obtain the first location of the first user when the first user needs to undergo an offline examination; A third obtaining unit configured to obtain a first doctor sub-database according to the first location. The first doctor sub-database includes doctor information within a first block, where the first location is within the first block; A fourth obtaining unit configured to perform feature extraction on the symptom description information of the first user to obtain first keyword information; A fifth obtaining unit configured to obtain first doctor information from the first doctor sub-database according to the first keyword information; A sixth obtaining unit configured to obtain a first examination prescription according to the symptom description information. The first examination prescription is an examination prescription issued by the first doctor; A seventh obtaining unit configured to obtain first examination appointment information according to the first examination prescription. The first examination appointment information is the examination appointment information of the first user at the hospital where the first doctor is located; An eighth obtaining unit configured to obtain a first examination result; A ninth obtaining unit configured to obtain first diagnosis and treatment information according to the first examination result and the symptom description information; A tenth obtaining unit configured to obtain the authentication information of the first doctor for the first diagnosis and treatment information according to the first diagnosis and treatment information.

8. An intelligent medical online service system, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, When the processor executes the program, it implements the steps of the method according to any one of claims 1-6.

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