Online diagnosis method and device based on artificial intelligence, storage medium and equipment

Abstract

The application relates to the technical field of medical information, and discloses an online consultation method and device based on artificial intelligence, a storage medium and electronic equipment, which comprises the following steps: in response to the authorization of a user, obtaining target user consultation information, the target user consultation information being information generated after communication with the target user; based on a pre-established decision model, determining the data portrait of the target user according to the target user consultation information, the decision model being a model generated by mining and analyzing big data in a database based on a clustering algorithm and used for accurately determining the disease type of the target user to determine the data portrait of the target user. Through the method, the technical problem of low online consultation efficiency can be solved.

Description

Technical Field

[0001] This invention relates to the field of medical information technology, and in particular to an online consultation method, device, storage medium and equipment based on artificial intelligence. Background Technology

[0002] With the development of internet technology, online consultation platforms have gradually emerged, and more and more people are inclined to consult doctors online for convenience, and then have doctors make diagnoses and prescribe medications.

[0003] Currently, the online consultation process mainly involves the following steps: First, the user describes their symptoms to the doctor on the consultation platform; second, the doctor prescribes a prescription based on the patient's description; finally, the user purchases the medication online, completing the consultation. However, in practice, it has been found that some users have very mild symptoms and do not require a prescription at all. These users' consultations are often focused on understanding how to prevent related diseases or seeking information about their causes. Furthermore, some users have similar symptoms and receive the same prescriptions. Throughout the entire online consultation process, from receiving the user to issuing the prescription, the doctor needs to be involved throughout, investing a significant amount of time, leading to low efficiency in online consultations. Summary of the Invention

[0004] This application provides an online consultation method, apparatus, storage medium, and device based on artificial intelligence to solve the technical problem of low efficiency in online consultations.

[0005] Firstly, it provides an online consultation method based on artificial intelligence, including:

[0006] In response to the user's authorization, the system obtains the target user's medical consultation information, which is generated after communicating with the target user.

[0007] Based on a pre-established decision model, a data profile of the target user is determined according to the target user's medical consultation information. The decision model is a model generated by mining and analyzing big data in the database based on a clustering algorithm, which is used to accurately determine the disease type of the target user and thus determine the data profile of the target user.

[0008] Secondly, an online consultation device based on artificial intelligence is provided, including:

[0009] Acquisition module: Used to acquire the target user's consultation information in response to the user's authorization. The target user's consultation information is generated after communicating with the target user.

[0010] The data profiling module is used to determine the data profile of the target user based on the pre-established decision model and the target user's medical information. The decision model is a model generated by mining and analyzing big data in the database based on a clustering algorithm. It is used to accurately determine the disease type of the target user in order to determine the data profile of the target user.

[0011] Thirdly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the aforementioned AI-based online consultation method.

[0012] Fourthly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-mentioned online consultation method based on artificial intelligence.

[0013] The aforementioned AI-based online consultation methods, devices, storage media, and electronic devices achieve precise patient profiling by mining and analyzing a large amount of historical user consultation data and using clustering algorithms to determine data profiles for various disease types. Simultaneously, this technical solution can, based on patient data profiles, identify to a large extent which patients truly need consultation and prescriptions, and which only require simple inquiries. Furthermore, when a patient truly needs consultation and prescriptions, AI can automatically generate a reference prescription, further improving the efficiency of online consultations and saving doctors' resources. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of an application environment for an online consultation method based on artificial intelligence in one embodiment of the present invention;

[0016] Figure 2 This is a flowchart illustrating an online consultation method based on artificial intelligence in one embodiment of the present invention;

[0017] Figure 3 This is a flowchart illustrating a method for establishing a decision model in one embodiment of the present invention;

[0018] Figure 4 This is a schematic diagram of big data mining and analysis processing in one embodiment of the present invention;

[0019] Figure 5 This is a schematic diagram illustrating historical user information in one embodiment of the present invention;

[0020] Figure 6 This is a data clustering effect diagram in a data profile of disease types determined based on a clustering algorithm in one embodiment of the present invention;

[0021] Figure 7 This is a schematic diagram of an online consultation device based on artificial intelligence in one embodiment of the present invention;

[0022] Figure 8 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The AI-based online consultation method provided in this invention can be applied to, for example... Figure 1 In the illustrated application environment, the client communicates with the server via a network. The server can obtain the target user's medical information from the client. This information can be generated using artificial intelligence technology; for example, an intelligent communication module can be pre-installed on the client. After the target user engages in intelligent communication, medical information is generated and transmitted to the server. Upon receiving the medical information, the server imports it into a pre-established decision model. This model then determines the target user's data profile, thereby identifying the target user's disease type.

[0025] In this embodiment of the invention, the decision model in the server-side, through mining and analysis of a large amount of historical user consultation data and based on clustering algorithms, determines data profiles for various disease types, thereby achieving accurate patient profiling and further improving the efficiency of online consultations. The client-side can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server-side can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0026] Please see Figure 2 As shown, Figure 2 This is a flowchart illustrating an online consultation method based on artificial intelligence according to an embodiment of the present invention, including the following steps:

[0027] S10: In response to the user's authorization, obtain the target user's medical consultation information, which is generated after communicating with the target user;

[0028] First, to obtain the user's authorization for personal information, the system will prompt the user to authorize the relevant consultation information. This consultation information primarily consists of the user's described symptoms, but may also include basic personal information such as gender and age to improve the accuracy of data profiling. Based on the user's authorization, the system obtains the target user's consultation information. In this embodiment, artificial intelligence technology can be used to communicate with the target user, thereby obtaining their consultation information and improving the efficiency of online consultations. For example, the target user's client can use voice or text to inquire about their symptoms, daily routine, and previous medical examinations. The target user answers these inquiries and can also ask related questions, engaging in intelligent communication. After the communication ends, the target user's client stores the communication record information. It can also extract and save useful information from the communication record information by searching for keywords.

[0029] S20: Based on a pre-established decision model, determine the target user's data profile according to the target user's consultation information. The decision model is a model generated by mining and analyzing big data in the database based on a clustering algorithm. It is used to accurately determine the target user's disease type in order to determine the target user's data profile.

[0030] After the intelligent communication with the target user concludes, the client can, on the one hand, transmit the complete communication information to the server for processing in a pre-established decision-making model; on the other hand, the client can also transmit extracted useful information to the server for import into the pre-established decision-making model. The extracted useful information may include the target user's description of symptoms such as "red spots on the face, itchy skin, preference for spicy food, and irregular sleep patterns." After this target user's consultation information is imported into the pre-established decision-making model, the model compares it one-to-one with the data in the model database. Based on the target user's consultation information, it determines which data profile in the decision-making model has the highest similarity to the target user, thus determining the target user's data profile. The aforementioned decision-making model is based on a clustering algorithm, generated by mining and analyzing large amounts of data in the database. It is used to accurately determine the target user's disease type, thereby determining the target user's data profile. Please refer to [link to relevant documentation]. Figure 3 As shown, Figure 3 This is a flowchart illustrating a method for establishing a decision model according to an embodiment of the present invention, including the following steps:

[0031] S21: In response to the user's authorization, obtain historical user information, which includes: historical user disease information and consultation information, and the historical user consultation information includes: historical user symptom information and personal basic information;

[0032] Decision-making models are generated by mining and analyzing big data in databases; therefore, acquiring a large amount of data is essential. Ping An's online consultation service has been operating for nearly 10 years and has stored a vast amount of patient consultation information, prescription data, and other data. Based on this large amount of data, and after obtaining user authorization, a series of processing steps are required to ensure high data quality. Please refer to [link / reference]. Figure 4 As shown, Figure 4 This is a schematic diagram of big data mining and analysis processing in one embodiment of the present invention, specifically including processing methods such as data integrity detection, data normalization, feature attribute simplification, and data segmentation. Data integrity detection can employ existing techniques such as parity checking, XOR checking, and redundancy checking; data normalization can employ existing techniques such as decimal scaling normalization and min-max normalization.

[0033] After processing the raw data, the next step is data analysis. For a better understanding of the specifics of the data being analyzed, please refer to [link to relevant documentation]. Figure 5 As shown, Figure 5 This is a schematic diagram illustrating historical user information in one embodiment of the present invention. The historical user information mainly includes: historical user disease information and consultation information. It should be noted that the decision model is generated by mining and analyzing data from patients who have already been diagnosed by doctors. Therefore, the historical user information includes historical user disease information, i.e., information on the diagnosed type of disease and its severity. Simultaneously, to accurately determine the symptoms exhibited by patients with a certain disease, the historical user consultation information includes: historical user symptom information and historical user basic personal information. The historical user symptom information includes symptom characteristics and lifestyle characteristics. Taking fatty liver as an example, symptom characteristics could include congenital information about abnormal liver function, and lifestyle characteristics could include information such as drinking alcohol, smoking, and lack of exercise. The historical user basic personal information mainly includes gender, age, etc., used to summarize whether a certain type of disease is concentrated in a certain age group, or more likely to occur in men or women, thereby achieving a more accurate data profile.

[0034] Optionally, historical user information also includes historical user prescription information, used to summarize medication use for different disease types, thereby determining which disease types require corresponding prescriptions. It should be noted that each disease has varying degrees of severity and urgency. When disease symptoms are mild, some historical user records will not contain historical user prescription information, meaning that this level of disease does not require a prescription. If some historical user records contain historical user prescription information, it indicates that a prescription is required when the corresponding symptoms appear. Furthermore, it should be noted that even if two patients have the same type of disease and both require prescriptions, their medication use will differ due to variations in symptoms. Therefore, it is necessary to summarize medication use for each disease type based on historical user prescription information, combined with historical user disease information and consultation information.

[0035] S22: Based on historical user disease and symptom information, and using a clustering algorithm, the symptom characteristics and lifestyle characteristics of each disease type are statistically analyzed in sequence.

[0036] After the above processing of the original data, the existing historical user information mainly includes: historical user disease information and consultation information. Meanwhile, the historical user consultation information mainly includes: historical user symptom information and basic personal information. In this embodiment of the invention, clustering algorithms can be used to sequentially divide the processed data into different categories, thereby discovering common groups. Clustering algorithms can employ existing iterative clustering analysis algorithms, graph theory clustering algorithms, etc., and this invention is not limited to these. For example, the processed historical user disease and symptom information can be classified and summarized using clustering algorithms to depict the symptom characteristics and lifestyle characteristics of a certain disease type. Please refer to [link to relevant documentation]. Figure 6 As shown, Figure 6 This is a data clustering effect diagram in a data profile of each disease type determined by a clustering algorithm in one embodiment of the present invention.

[0037] S23: Based on historical user disease information and personal basic information, and using a clustering algorithm, the age distribution and male-female ratio of each disease type are statistically analyzed.

[0038] S24: Based on the symptom characteristics, lifestyle characteristics, age distribution, and male-female ratio of each disease type, determine the data profile of each disease type.

[0039] Through the above data classification, the symptom characteristics, lifestyle characteristics, age distribution, and male-female ratio of each disease type are presented. Based on these characteristics, the data profile of each disease type is finally determined.

[0040] Optionally, after determining the target user's data profile in step S20, the system can also determine whether a prescription needs to be issued to the target user based on the target user's data profile and the historical user prescription database in the decision model. If it is determined that a prescription is not needed, the system will switch to the consultation interface; otherwise, it will switch to the prescription issuance interface. For example, some users have very mild symptoms and do not need a prescription at all. These users' purpose for seeking medical advice is often focused on understanding how to prevent related diseases or popular science knowledge about the causes of diseases. When the decision model determines that the target user does not need a prescription, the consultation interface can be invoked, and communication can be conducted through artificial intelligence technology or other auxiliary personnel, without the need for a doctor to receive the patient, further improving the efficiency of online consultation.

[0041] Meanwhile, when the decision model determines that a target user needs a prescription, the prescription issuance interface can be invoked, allowing the doctor to issue a prescription based on the determined target user data profile. Alternatively, after accessing the prescription issuance interface, a reference prescription for the target user can be generated based on the historical user prescription database within the decision model, for the doctor's reference. These technical solutions save doctors significant time and improve the efficiency of online consultations.

[0042] Optionally, before determining the target user's data profile in step S20, it can be determined whether the target user's data profile can be determined based on the pre-established decision model, according to the target user's consultation information. If the target user's data profile cannot be determined, the process proceeds to the doctor's consultation interface. It should be noted that, due to database limitations, the data profile in the decision model can only include data profiles of currently common disease types. As times change, new disease types may emerge, and the decision model may not be able to determine the target user's data profile. In this case, it can be determined in advance, based on the target user's consultation information, whether the target user's data profile can be determined based on the pre-established decision model. In this embodiment of the invention, determining whether the target user's data profile can be determined based on the pre-established decision model, according to the target user's consultation information, includes the following steps:

[0043] A1: Determine the similarity value of the predefined target user data profile;

[0044] A2: Based on a pre-established decision-making model, the target user information is compared with the data profiles of each disease type in turn, and the similarity value is calculated.

[0045] A3: If the maximum calculated similarity is less than the determined similarity value of the target user's data profile, then it is determined that the target user's data profile cannot be determined; otherwise, the target user's data profile is determined based on the data profile of the disease type corresponding to the maximum similarity value.

[0046] First, a predetermined similarity value is defined for the target user's data profile, for example, 70%. Second, based on a pre-established decision model, the target user information is compared sequentially with data profiles for each disease type, and the similarity values ​​are calculated. For example, after comparison, the calculated similarity values, from largest to smallest, are: 75%, 69%, 42%, 25%, etc. Finally, a judgment is made. If the maximum calculated similarity value is less than the predetermined similarity value of the target user's data profile, it is determined that the target user's data profile cannot be determined. Otherwise, the target user's data profile is determined based on the data profile of the disease type corresponding to the maximum similarity value. In this case, the maximum calculated similarity value is 75%, which is greater than the predetermined similarity value of 70% for the target user's data profile. Therefore, it is determined that the target user's data profile can be determined based on the pre-established decision model, and the target user's data profile is the data profile of the disease type corresponding to the maximum similarity value.

[0047] As can be seen, the above solution mines and analyzes a large amount of historical user consultation and disease data, and uses clustering algorithms to determine data profiles for various disease types, thereby achieving accurate patient profiling. Simultaneously, this technical solution can, based on patient data profiles, identify to a large extent which patients truly need consultations and prescriptions, and which patients only require simple inquiries. Furthermore, when a patient truly needs a consultation and prescription, it can automatically generate a reference prescription based on artificial intelligence, further improving the efficiency of online consultations and saving doctors' resources.

[0048] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. In addition, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to".

[0049] In one embodiment, an artificial intelligence-based online consultation device is provided, which corresponds one-to-one with the artificial intelligence-based online consultation method described in the above embodiments. For example... Figure 7 As shown, the online consultation device includes: an acquisition module 301, a judgment module 302, a data profile determination module 303, and a generation module 304. Detailed descriptions of each functional module are as follows:

[0050] Acquisition module 301: In response to user authorization, acquire the target user's consultation information, which is generated after communication with the target user.

[0051] Judgment module 302: It is used to determine whether the target user's data profile can be determined based on the pre-established decision model according to the target user's consultation information. If the target user's data profile cannot be determined, it will be transferred to the doctor consultation interface.

[0052] The data profiling module 303 is used to determine the data profile of the target user based on a pre-established decision model and the target user's medical information. The decision model is a model generated by mining and analyzing big data in the database based on a clustering algorithm, and is used to accurately determine the disease type of the target user in order to determine the data profile of the target user.

[0053] Generation module 304: Used to determine whether a prescription needs to be issued to the target user based on the target user's data profile and through the historical user prescription database in the decision model;

[0054] If it is determined that no prescription needs to be issued to the target user, the system will switch to the consultation interface; otherwise, it will switch to the prescription issuance interface and generate a reference prescription for the target user based on the historical user prescription database in the decision model.

[0055] In one embodiment, the data profiling module 303 is further specifically used for establishing a decision model, including:

[0056] In response to user authorization, historical user information is obtained, including: historical user disease information and consultation information;

[0057] Based on historical user disease information and consultation information, data profiles for each disease type are determined by extracting key information and using clustering algorithms.

[0058] In one embodiment, the data profiling module 303 is further specifically used for establishing a decision model, including:

[0059] Based on historical user disease and symptom information, and using clustering algorithms, the symptom characteristics and lifestyle characteristics of each disease type are statistically analyzed in sequence.

[0060] Based on historical user disease information and personal basic information, and using clustering algorithms, the age distribution and gender ratio of each disease type were statistically analyzed.

[0061] Based on the symptom characteristics, lifestyle characteristics, age distribution, and male-female ratio of each disease type, a data profile for each disease type is determined.

[0062] In one embodiment, the data profiling module 303 is further specifically used for establishing a decision model, including:

[0063] Based on historical user prescription information, key information is extracted and the prescription information is classified using a clustering algorithm to summarize the medication use for each disease type.

[0064] This invention provides an artificial intelligence-based online consultation device. By mining and analyzing a large amount of historical user consultation and disease data, and based on clustering algorithms, it determines data profiles for various disease types, thereby achieving precise patient profiling. Simultaneously, this technical solution can, based on patient data profiles, identify to a large extent which patients truly need consultation and prescriptions, and which patients only require simple inquiries. Furthermore, when a patient truly needs consultation and prescriptions, it can automatically generate a reference prescription based on artificial intelligence, further improving the efficiency of online consultations and saving doctors' resources.

[0065] For specific limitations regarding online consultation devices, please refer to the limitations of AI-based online consultation methods mentioned above, which will not be repeated here. Each module in the aforementioned online consultation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0066] In one embodiment, a computer device is provided, the internal structure of which can be shown as follows: Figure 8 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of an artificial intelligence-based online consultation method.

[0067] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0068] In response to the user's authorization, the system obtains the target user's medical consultation information, which is generated after communicating with the target user.

[0069] Based on a pre-established decision model, a data profile of the target user is determined according to the target user's medical consultation information. The decision model is a model generated by mining and analyzing big data in the database based on a clustering algorithm, which is used to accurately determine the disease type of the target user and thus determine the data profile of the target user.

[0070] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, performs the following steps:

[0071] In response to the user's authorization, the system obtains the target user's medical consultation information, which is generated after communicating with the target user.

[0072] Based on a pre-established decision model, a data profile of the target user is determined according to the target user's medical consultation information. The decision model is a model generated by mining and analyzing big data in the database based on a clustering algorithm, which is used to accurately determine the disease type of the target user and thus determine the data profile of the target user.

[0073] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0074] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0076] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An online consultation method based on artificial intelligence, characterized in that, include: In response to the user's authorization, the system obtains the target user's medical consultation information, which is generated after communicating with the target user. Based on a pre-established decision model, a data profile of the target user is determined according to the target user's medical consultation information. The decision model is a model generated by mining and analyzing big data in the database based on a clustering algorithm. It is used to accurately determine the disease type of the target user in order to determine the data profile of the target user. The establishment of the decision-making model includes the following steps: In response to user authorization, historical user information is obtained, including: historical user disease information and consultation information; Based on historical user disease information and consultation information, data profiles for each disease type are determined by extracting key information and using clustering algorithms. The historical user consultation information includes: historical user symptom information and basic personal information. The step of determining data profiles for each disease type based on a clustering algorithm, using key information extracted from historical user disease information and consultation information, includes: Based on historical user disease and symptom information, and using clustering algorithms, the symptom characteristics and lifestyle characteristics of each disease type are statistically analyzed in sequence. Based on historical user disease information and personal basic information, and using clustering algorithms, the age distribution and gender ratio of each disease type were statistically analyzed. Based on the symptom characteristics, lifestyle characteristics, age distribution, and male-female ratio of each disease type, a data profile for each disease type is determined.

2. The method according to claim 1, characterized in that, The historical user information also includes historical user prescription information, used to determine that certain disease types require corresponding prescriptions. After determining the data profile for each disease type based on a clustering algorithm by extracting key information from historical user disease information and consultation information, the process further includes: Based on historical user prescription information, key information is extracted and the prescription information is classified using a clustering algorithm to summarize the medication use for each disease type.

3. The method according to claim 1, characterized in that, After determining the target user's data profile based on the pre-established decision-making model and the target user's consultation information, the process also includes: Based on the data profile of the target user and through the historical user prescription database in the decision model, it is determined whether a prescription needs to be issued to the target user. If it is determined that no prescription needs to be issued to the target user, the user will be redirected to the consultation interface; otherwise, the user will be redirected to the prescription issuance interface.

4. The method according to claim 3, characterized in that, After transferring to the prescription issuance interface, the method further includes: generating a reference prescription for the target user based on the historical user prescription database in the decision model.

5. The method according to claim 1, characterized in that, Before determining the target user's data profile based on the pre-established decision model and the target user's consultation information, the process also includes: Based on the target user's consultation information, determine whether the target user's data profile can be determined based on the pre-established decision model. If the target user's data profile cannot be determined, then transfer to the doctor's consultation interface.

6. An online consultation device based on artificial intelligence, characterized in that, include: Acquisition module: Used to acquire the target user's consultation information in response to the user's authorization. The target user's consultation information is generated after communicating with the target user. The data profile determination module is used to determine the data profile of the target user based on the pre-established decision model and the target user's medical information. The decision model is a model generated by mining and analyzing big data in the database based on a clustering algorithm. It is used to accurately determine the disease type of the target user in order to determine the data profile of the target user. The establishment of the decision-making model includes the following steps: In response to user authorization, historical user information is obtained, including: historical user disease information and consultation information; Based on historical user disease information and consultation information, data profiles for each disease type are determined by extracting key information and using clustering algorithms. The historical user consultation information includes: historical user symptom information and basic personal information. The step of determining data profiles for each disease type based on a clustering algorithm, using key information extracted from historical user disease information and consultation information, includes: Based on historical user disease and symptom information, and using clustering algorithms, the symptom characteristics and lifestyle characteristics of each disease type are statistically analyzed in sequence. Based on historical user disease information and personal basic information, and using clustering algorithms, the age distribution and gender ratio of each disease type were statistically analyzed. Based on the symptom characteristics, lifestyle characteristics, age distribution, and male-female ratio of each disease type, a data profile for each disease type is determined.

7. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the method of any one of claims 1 to 5 when it is run.

8. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1 to 5.

Images