WO2020213843A1 - Système de fourniture d'informations médicales personnalisées par l'utilisateur et son procédé de fonctionnement - Google Patents

Système de fourniture d'informations médicales personnalisées par l'utilisateur et son procédé de fonctionnement Download PDF

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Publication number
WO2020213843A1
WO2020213843A1 PCT/KR2020/003867 KR2020003867W WO2020213843A1 WO 2020213843 A1 WO2020213843 A1 WO 2020213843A1 KR 2020003867 W KR2020003867 W KR 2020003867W WO 2020213843 A1 WO2020213843 A1 WO 2020213843A1
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medical
information
user
diagnosis
history
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PCT/KR2020/003867
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English (en)
Korean (ko)
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이정의
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이정의
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the present invention relates to a user-customized medical information providing system and a driving method thereof, and more particularly, to a user-customized medical information providing system and a driving method thereof to which deep learning technology of artificial intelligence is applied.
  • Machine learning refers to intelligence created by machines or software (SW) capable of cognitive abilities, reasoning abilities, learning abilities, and comprehension abilities, which are considered uniquely possessed by humans. It is collectively referred to as the field of science that studies how to make computers or computer SW that can perform intelligent functions. It mainly includes pattern recognition, expert system, natural language processing, machine learning and automatic control technology.
  • a platform that provides services related to securities investment and transactions, and artificial intelligence technology that uses artificial intelligence to grasp various information and provide customized investment-related information to service users have been introduced.
  • Such conventional recommendation methods include a simple recommendation method using content sales volume and recommendation information of buyers, and a customized recommendation method using preference genre information or preference category information input by each user, or purchase history of each user.
  • Patent Document 1 KR10-0739570 B1
  • Patent Document 2 KR10-1525576 B1
  • Patent Document 3 KR10-0538573 B1
  • the present invention has been derived from such a technical background, and an object thereof is to solve a problem in which the imbalance of medical information is not resolved compared to the speed of medical technology development.
  • the present invention for achieving the above object includes the following configuration.
  • the medical information providing system matches the text extracted from the information receiving unit receiving user information and symptom information from the user terminal and symptom information received by the information receiving unit with information stored in big data. Includes a diagnostic unit that identifies a diagnosis name according to a symptom matched by a set reference matching rate or higher, and an information providing unit that provides information necessary for treatment including at least one recommended hospital or department of treatment to the user terminal, and the diagnosis name identified by the diagnosis unit. Characterized in that.
  • the driving method of the user-customized medical information providing system is that the user terminal, which is mounted and driven by a medical information providing service app, is provided by the user through the medical information providing service app.
  • medical consumers can take the lead in health care by solving the problem of increasing medical care by medical staff and indiscriminate medical shopping by medical service users by accurately delivering medical knowledge about what medical treatment and treatment are necessary. The effect that enables it is derived.
  • the medical information providing service system and its driving method according to an embodiment of the present invention, by providing customized medical information that medical consumers (patients) can easily access, improving the quality of life and at the same time social It is expected to bring about reduction of indirect capital.
  • the present invention not only can it be possible to provide precise medical services by providing user-specific information based on big data analysis based on information such as the user's medical history, medical history, family history, etc., but also the user's specific information (existing medical history, It is possible to prevent medical accidents due to lack of user information by collecting information such as biographical history, side effects, etc., and transmitting the user's information when the user consults with a health care provider.
  • FIG. 1 is a block diagram showing the configuration of a user-customized medical information providing system according to an embodiment of the present invention
  • FIG. 2 is a block diagram showing in more detail the configuration of a medical information providing service server according to an embodiment of the present invention
  • FIG. 3 is a flowchart illustrating a method of driving a system for providing customized medical information according to an embodiment of the present invention
  • FIG. 4 is an exemplary diagram for explaining an embodiment of a driving method of a system for providing customized medical information according to an embodiment of the present invention.
  • FIG. 1 is a block diagram showing the configuration of a user-customized medical information providing system according to an embodiment of the present invention.
  • a user-customized medical information providing system includes a user terminal 10, a medical information providing service server 20, a medical information server 30, and a location identification server 40. Includes.
  • the user terminal 10 is an IP-allocated terminal and can perform network communication through the Internet or the like.
  • a wireless communication device with guaranteed portability and mobility, navigation, PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) terminal, smartphone, smart phone It may include all kinds of handheld-based wireless communication devices such as a smartpad and a tablet PC.
  • a desktop PC a slate PC, a notebook computer, and a portable multimedia player (PMP) may be applicable.
  • PMP portable multimedia player
  • the terminal to which the present invention is applicable is not limited to the above-described types, and it is natural that all terminals capable of communicating with external devices may be included.
  • the user terminal 10 is driven by being equipped with a medical information providing service-only app, and through the medical information providing service app, the user's age, gender, pregnancy status, specifics, family history, medical history, biographies, work User information and symptom information including at least one of the environments are input.
  • an app dedicated to providing medical information may provide a convenient user interface for inputting symptoms.
  • a screen for selecting a part of the body part is provided, and a screen for selecting in more detail the symptoms that may occur in the body part selected step by step is further provided.
  • the user terminal 10 includes a communication module that performs communication with the medical information providing service server 20 including a communication module.
  • the communication module is a communication module supporting a short-range wireless communication method between devices as well as a communication module supporting a communication method using a communication network (for example, a mobile communication network, wired Internet, wireless Internet, and broadcasting network) that the network may include. Can also be included.
  • the medical information providing service server 20 includes a computer device or a server device that performs primary diagnosis.
  • the medical information providing service server 20 since it needs to process a vast amount of data, it may be implemented as a parallel computing system or a cloud computing environment in which at least one computer is connected in parallel.
  • the medical information providing service server 20 uses various algorithms of artificial intelligence.
  • the medical information providing service server 20 is connected to the user terminal 10 through a network through a wired/wireless communication network to transmit and receive data.
  • the network is one of networks such as PAN (personal area network), LAN (local area network), CAN (campus area network), MAN (metropolitan area network), WAN (wide area network), BBN (broadband network), and the Internet. It may include one or more of any network.
  • the network may include any one or more of a network topology including a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree, or a hierarchical network, but is not limited thereto. Does not.
  • the medical information providing service server 20 receives user information and symptom information from the user terminal 10, matches the text extracted from the received symptom information with information stored in big data, and matches a symptom that matches a preset reference matching rate or higher.
  • the diagnosis name according to is identified, the identified diagnosis name is provided to the user terminal, and at least one hospital or medical department required for treatment is recommended.
  • the medical information providing service server 20 learns in a supervised learning method.
  • Supervised learning is a method of machine learning to infer a function from training data.
  • the learning dataset used by the medical information providing service server 20 may include, for example, personal disease history and personal medical history information among the user's yearly medical data provided by the KNHIS database.
  • values of personal disease history and personal medical history are set as a learning dataset based on the user's medical data for each year, and learned through supervised learning.
  • the values of the personal disease history and personal medical history are set as input values and relearned.
  • the medical information providing service server 20 employs an artificial intelligence system.
  • Artificial intelligence systems include control structures, knowledge bases, databases, and inference engines.
  • the control structure translates the rules of various algorithms with a rule translator.
  • the knowledge base contains various artificial intelligence algorithms as rules.
  • the inference engine may classify data in the database and classify input images using rules translated by an expert rule translator.
  • the knowledge base includes rules for detecting medical lesions
  • the database includes a second diagnosis result in which the expert diagnosis result is added to the artificial intelligence-based first diagnosis result. Re-diagnosis and re-learning are performed using together.
  • the medical information providing service server 20 receives the final diagnosis and classification result from the user terminal 10 after providing the second diagnosis result to the user terminal 10 and stores it in the database 25 as a final decision.
  • the final diagnosis and classification result may be a prescription image received after a user directly visits a hospital.
  • prescription information issued to the user may be directly received from the hospital.
  • a process of obtaining consent from the user or the hospital for provision of prescription information should be preceded.
  • the final diagnosis and classification results can be stored and stored together with the AI analysis results, and then used for re-learning and performance evaluation.
  • relearning includes a process of labeling and using the input value as the result of the final diagnosis and classification that has been finally confirmed by the user.
  • the medical information server 30 stores symptom information on various diseases including big data.
  • the medical information providing service server 20 may be interlocked with the medical information server 30.
  • the medical information server 30 includes health care big data. For example, it may include information obtained by extracting the search results of the portal site for recommendation treatment by symptoms and converting them into a database. In addition, it may include information obtained by converting treatment by department, search results for examination items, or treatment history of doctors into a database.
  • the medical information server 30 includes a KNHIS database.
  • the medical information server 30 may collect health care big data from a portal site or a medical institution server such as a hospital or pharmacy using web crawling.
  • the location determination server 40 is interpreted to encompass a technical configuration for identifying the location information of the user terminal 10.
  • the location detection server 40 may receive a GPS signal from the user terminal 10 to obtain location information of the user terminal 10.
  • the location determination server 40 may be implemented as a communication company server. That is, the location determination server 40 is interpreted to encompass a technical configuration capable of identifying the current location information of the user terminal 10 in various technical methods.
  • the location identification server 40 identifies the current location information of the user terminal 10 and provides medical information. It is provided to the service server 20.
  • the medical information providing service server 20 identifies the location information of the user terminal 10 from the location detection server 40, and uses the location information of the identified user terminal 10 to provide medical treatment. Recommend information.
  • the medical information providing service server 20 provides a diagnosis name according to symptom information received from the user terminal 10 and a prescription input from the recommended hospital or treatment department based on deep learning technology. By comparing and calculating the accuracy of the diagnosis name (disease code, treatment code), the diagnosis name according to the symptom is learned according to symptom information received from the user terminal 10.
  • Deep learning is a set of machine learning algorithms that attempt a high level of abstraction through a combination of several nonlinear transformation methods.
  • abstraction refers to the work of summarizing key contents or functions in a large amount of data or complex data.
  • the medical information providing service server 20 provides the same symptom and diagnosis name as the previous user based on the diagnosis name according to the symptom identified by the symptom information received from the user terminal 10. Provide more information on the prescription history of other patients.
  • the user's age or whether or not other diseases have occurred may be investigated together. For example, in the case of a runny nose from throat, information on changes in medications administered for the disease is provided based on treatment history information stored in big data.
  • the treatment history information stored in the big data is prescription information directly registered by another person in the medical information providing service server 20 according to an embodiment of the present invention, or by web crawling that is personally shared through web pages such as cafes and blogs. It may be collected information. Also, it may be based on information obtained by converting a search result of a portal site and recommendation treatment for each symptom collected by the medical information server 30 into a database.
  • the medical staff it is possible for the medical staff to make a more thoughtful diagnosis to each patient, and the patient particle can go through a verification procedure based on the actual treatment history information of other patients who complained of the same symptoms before and can present their opinions. It becomes possible to receive medical treatment.
  • the medical information providing service-only app of the user terminal 10 includes hospital information recommended to the user or the name information on the medical department or the identified disease according to the information received from the medical information providing service server 20. It can be printed as a list.
  • a reservation request may be received by selecting information of a hospital, a medical department, or a person in charge from the user.
  • the medical information providing service server 20 is a doctor selected by the user through the selected hospital homepage or treatment department homepage. It is possible to request a medical appointment for and further upload the user information identified based on the information received from the user terminal 10 to the hospital homepage or the treatment department homepage.
  • the user information including symptom information already entered by the user or at least one of age, gender, pregnancy status, peculiarities, family history, medical history, medical history, and work environment is delivered to the hospital or department of the appointment. I can. Accordingly, it is possible to perform a simple interview, thereby simplifying the cumbersome process of having to go through each interview in the first hospital.
  • variable value it is possible to receive user information and set it as a variable value, and by using this, it is possible to provide more personalized medical information by deriving the difference between the previously learned personal disease history and the treatment department by symptom according to the personal medical history. And you can see which variable values had more influence.
  • FIG. 2 is a block diagram showing in more detail the configuration of a medical information providing service server according to an embodiment of the present invention.
  • the medical information providing service server includes a communication unit 200, an information receiving unit 210, a diagnosis unit 220, an information providing unit 230, and a learning unit 240.
  • the communication unit 200 performs data communication with the user terminal 10, the medical information server 30, and the location server 40.
  • the communication unit 200 includes not only a communication module supporting a communication method using a communication network (for example, a mobile communication network, wired Internet, wireless Internet, broadcasting network), but also a short-range wireless communication method between devices. Modules can also be included.
  • the information receiving unit 210 receives user information and symptom information from the user terminal 10 through the communication unit 200.
  • the information receiving unit 210 is provided with an app for providing medical information service through the communication unit 200 from the user terminal 10, which is operated by the user through an app dedicated to providing medical information.
  • User information and symptom information including at least one of medical history, biographical history, and work environment are received.
  • a variety of information that can identify the user's overall physical condition is received, such as drug allergic reactions, living environment, dietary habits, and drug information.
  • the diagnosis unit 220 identifies a diagnosis name according to a symptom matching a predetermined reference matching rate or higher by matching text extracted from symptom information received by the information receiving unit 210 with information stored in big data.
  • the information providing unit 230 provides the user terminal 10 with information necessary for treatment including a diagnosis name identified by the diagnosis unit 220 and at least one recommended hospital or department.
  • the learning unit 240 compares the diagnosis name according to symptom information received from the user terminal 10 and a prescription input from the recommended hospital or treatment department based on deep learning technology, and the accuracy of the diagnosis name (disease code, treatment code) A diagnosis name according to symptoms is learned according to symptom information received from the user terminal 10 by calculating.
  • the learning unit 240 may learn through supervised learning by setting personal disease history and personal medical history information among the user's yearly medical data stored in big data as a learning dataset.
  • FIG. 3 is a flowchart illustrating a method of driving a system for providing customized medical information according to an embodiment of the present invention.
  • a user terminal that is equipped with a medical information providing service app installed and operated is a user including at least one of age, gender, pregnancy, special information, family history, medical history, biographical history, and work environment from the user through the medical information providing service app.
  • Information and symptom information are input (S300).
  • the medical information providing service server receives user information and symptom information from the user terminal (S310), and matches the text extracted from the received symptom information with information stored in big data, according to symptoms matching a preset reference matching rate or higher.
  • the diagnosis name is identified (S320).
  • the medical information providing service server determines the location information of the user terminal (S330).
  • the medical information providing service server provides the identified diagnosis name to the user terminal, and recommends at least one hospital or department that is necessary for treatment and capable of treatment by using the location information of the user terminal (S340).
  • the medical information providing service server compares the diagnosis name primarily identified based on the deep learning technology with the prescription input from the recommended hospital or department, and calculates the accuracy of the diagnosis name received from the user terminal.
  • the diagnosis name according to the symptoms is learned according to the symptom information (S350).
  • the medical information providing service server further provides information on the prescription history of another patient for the same symptom and diagnosis name previously based on the diagnosis name according to the user's symptom input from the user (S360).
  • the user's age or whether or not other diseases are onset may be investigated together to provide a prescription history of another patient corresponding to a similar situation.
  • information on changes in medications administered for the disease is provided based on treatment history information stored in big data.
  • the medical staff it is possible for the medical staff to make a more thoughtful diagnosis to each patient, and the patient particle can go through a confirmation procedure based on the treatment history information of other patients before and can provide an opinion, making it possible to receive highly reliable treatment. .
  • the medical information providing service server when a reservation request is received from an app dedicated to the medical information providing service of the user terminal (S370), the medical information providing service server performs a medical reservation with at least one of the recommended hospitals or treatment departments (S380). In addition, it is possible to recommend more specialists in the field. Then, according to the user's reservation request, a medical appointment can be made to the recommended specialist.
  • FIG. 4 is an exemplary view for explaining an embodiment of a driving method of a system for providing user-customized medical information according to an embodiment of the present invention.
  • the user may sign up and log in as a member through an app dedicated to providing medical information service running on the user terminal 10 (S400).
  • unique information including at least one of the user's age, gender, pregnancy status, special details, family history, medical history, biographical history, and work environment may be input in advance (S420, S422).
  • the unique information entered at the time of membership registration can be automatically recognized.
  • it is not limited thereto.
  • the medical information providing service server 20 requests health care big data from the medical information server 30 (S410), and collects health care information according to the request result (S415). Then, it is stored in a database provided in the medical information providing service server 20.
  • the user enters his or her basic symptom information after logging in through a dedicated app for providing medical information.
  • the medical information providing service server 20 may grasp the unique information input at the time of membership registration, and further grasp the additionally input symptom information.
  • the medical information providing service server 20 extracts metadata from the health care big data obtained from the medical information server 30 and the basic symptom information text of the user received from the user terminal 10 (S424).
  • the medical information providing service server 20 matches the text extracted from the received symptom information with the information stored in the health care big data, and extracts data matching a preset reference matching rate or higher.
  • the medical information providing service server 20 extracts data whose matching rate with text included in the user information is greater than or equal to the reference matching rate among health care big data and transmits it to the user terminal 10 (S426).
  • the user terminal 10 displays information received from the medical information providing service server 20 on a screen, and receives selection of additional information from the user through an app dedicated to the medical information providing service (S430).
  • the additional information may be information for identifying more detailed symptoms of the user.
  • the user terminal 10 transmits the selection information received from the user through the medical information providing service dedicated app to the medical information providing service server 20 (S432).
  • the medical information providing service server 20 stores and learns selection information received from the user terminal 10 (S434), and then extracts user preference information based on the learned information (S436).
  • the preference information may be part of information classified by criteria such as a location or characteristic of a medical institution that the user prefers.
  • the medical information providing service server 20 transmits the extracted user's preference information to the user terminal 10, and the user inputs user symptoms in detail through an app screen dedicated to the medical information providing service of the user terminal 10 ( S440).
  • the user may transmit a customized treatment and recommendation request to the medical information providing service server 20 through the medical information providing service dedicated app screen of the user terminal 10 (S450).
  • the medical information providing service server 20 extracts treatment department information for each user symptom based on the health care big data collected from the medical information server 30 (S452). In addition, the medical information providing service server 20 may further extract treatment content information for each treatment department based on the health care big data collected from the medical information server 30 (S454).
  • the medical information providing service server 20 extracts user-customized treatment department information based on the user information based on the extracted information (S456).
  • the medical information providing service server 20 requests the location-based information of the user terminal from the location determination server 40 (S460).
  • the location determination server 40 transmits the user location-based information to the medical information providing service server 20 (S462).
  • the medical information providing service server 20 generates a user location-based extraction information list by using the identified user location information (S464).
  • a user location-based extraction information list by using the identified user location information (S464).
  • the medical information providing service server 20 provides the user terminal 10 with a list including information on a recommended hospital or department generated by extracting user-customized treatment department information.
  • the medical information providing service server 20 provides the identified diagnosis name to the user terminal 10, and recommends at least one hospital or department that is necessary for treatment and capable of treatment by using the location information of the identified user terminal. will be.
  • the user After that, after visiting the hospital, the user registers a prescription issued from the actual hospital through the user terminal 10 (S470).
  • the prescription may be provided as a photographed image from the user terminal 10.
  • the user terminal 10 requests a prescription registration to the medical information providing service server 20 (S472).
  • the medical information providing service server 20 may request and receive prescription information for the user from a hospital or pharmacy server.
  • the medical information providing service server 20 may receive a prescription image file taken directly from the user terminal 10 at the same time as the prescription registration request.
  • the medical information providing service server 20 stores the obtained prescription in the database (S474).
  • the medical information providing service server 20 may provide prescription information prescribed by another person having the same disease or symptom as the user, even if the prescription is not necessarily issued by the user.
  • the medical information providing service server 20 compares the diagnosis name primarily identified based on the deep learning technology with a prescription input from the recommended hospital or medical department to determine the accuracy of the diagnosis name.
  • a diagnosis name according to a symptom is learned according to symptom information received from the user terminal 10 (S480).
  • More personalized medical information can be provided by setting the user's personal information including at least one of age, gender, pregnancy status, special matters, family history, medical history, biographical history, and work environment as a variable value and re-learning.
  • the above-described method may be implemented as an application or in the form of program instructions that may be executed through various computer components and recorded in a computer-readable recording medium.
  • the computer-readable recording medium may include program instructions, data files, data structures, etc. alone or in combination.
  • the program instructions recorded in the computer-readable recording medium may be specially designed and constructed for the present invention, and may be known and usable to those skilled in the computer software field.
  • Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magnetic-optical media such as floptical disks. media), and a hardware device specially configured to store and execute program instructions such as ROM, RAM, flash memory, and the like.
  • Examples of the program instructions include not only machine language codes such as those produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.
  • the hardware device may be configured to operate as one or more software modules to perform processing according to the present invention, and vice versa.

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Abstract

La présente invention se rapporte à un système de fourniture d'informations médicales personnalisées par l'utilisateur utilisant une technologie d'intelligence artificielle, et à son procédé de fonctionnement, le système de fourniture d'informations médicales comprenant : une unité de réception d'informations pour recevoir des informations d'utilisateur et des informations de symptôme en provenance d'un terminal d'utilisateur ; une unité de diagnostic, qui met en correspondance, avec des informations stockées dans des mégadonnées, un texte extrait des informations de symptôme reçues dans l'unité de réception d'informations, de sorte à identifier un nom de diagnostic en fonction du symptôme mis en correspondance avec au moins un taux de mise en correspondance de référence prédéfini ; et une unité de fourniture d'informations pour fournir, au terminal d'utilisateur, le nom de diagnostic identifié par l'unité de diagnostic, et les informations qui sont nécessaires pour un traitement et comprennent au moins un hôpital recommandé ou un bureau médical recommandé, et, ainsi, la présente invention résout le problème selon lequel un traitement excessif par un personnel médical et un achat médical aveugle par un utilisateur de service médical augmentent, ce qui permet de gérer activement la santé d'un consommateur médical.
PCT/KR2020/003867 2019-04-19 2020-03-20 Système de fourniture d'informations médicales personnalisées par l'utilisateur et son procédé de fonctionnement WO2020213843A1 (fr)

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KR102088980B1 (ko) * 2019-04-19 2020-03-13 이정의 사용자 맞춤형 의료정보 제공 시스템 및 이의 구동방법
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