WO2018105995A2 - Dispositif et procédé de prédiction d'informations de santé à l'aide de mégadonnées - Google Patents

Dispositif et procédé de prédiction d'informations de santé à l'aide de mégadonnées Download PDF

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WO2018105995A2
WO2018105995A2 PCT/KR2017/014159 KR2017014159W WO2018105995A2 WO 2018105995 A2 WO2018105995 A2 WO 2018105995A2 KR 2017014159 W KR2017014159 W KR 2017014159W WO 2018105995 A2 WO2018105995 A2 WO 2018105995A2
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Prior art keywords
health
score
information
disease
data
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English (en)
Korean (ko)
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WO2018105995A3 (fr
Inventor
이대호
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Onesoftdigm Co Ltd
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Onesoftdigm Co Ltd
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Priority claimed from KR1020170016988A external-priority patent/KR101970947B1/ko
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Priority to US16/465,543 priority Critical patent/US20200005944A1/en
Publication of WO2018105995A2 publication Critical patent/WO2018105995A2/fr
Publication of WO2018105995A3 publication Critical patent/WO2018105995A3/fr
Anticipated expiration legal-status Critical
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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
    • G16H50/20—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
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • 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
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising

Definitions

  • the present invention relates to an apparatus and method for predicting health information using big data, and more particularly, to an apparatus and method for predicting self health using big data.
  • Big data is a technology that collects and analyzes large amounts of data rapidly and effectively beyond the processing level of relational databases.
  • Major countries and global companies are focused on fostering and utilizing the big data industry. Companies are using big data directly and indirectly for their business, and the rate is expected to increase.
  • the National Institutes of Health is attempting to reform healthcare through the Pillbox project using big data, for example, a drug search site operated by the National Library of Medicine.
  • a drug search site operated by the National Library of Medicine.
  • the site can manage and predict diseases that are the main targets of management. It is possible.
  • An object of the present invention for solving the above problems is to provide an apparatus for predicting health information through big data.
  • An object of the present invention for solving the above problems is to provide a method for predicting health information through big data.
  • Health information prediction apparatus for achieving the above object, the medical examination unit for deriving the medical examination data, the diagnosis using the open personal health recording platform corresponding to big data, the diagnostic data
  • the health score calculator may calculate a health score for each department and a disease and an individual health score.
  • the apparatus may further include a future health score calculator configured to calculate a future health score for each disease of the user.
  • the apparatus may further include a health management unit for deriving an individual health index based on the personal health score and the future health score for each disease.
  • the method may further include a comparative analysis unit which compares and analyzes an environment group similar to a user based on the health score for each disease.
  • the apparatus may further include a service evaluation unit for deriving medical service evaluation information of a medical institution.
  • the health information prediction apparatus may further include a personal health record platform.
  • the personal health record platform includes an information collecting unit for collecting health and disease management knowledge, a feedback unit for feeding back with reference to the knowledge repository, an personal life health information record interface, an application programming interface for service integration and linkage, and structured data and unstructured data. It may include a data linking unit for linking, a cloud service unit for providing cloud computing services using big data, and a health prediction unit for predicting individual health management through public data of personal health records.
  • the present invention also provides a U-health care unit that provides real-time u-healthcare utilizing data related to health information, and a PHI monitor unit that provides monitoring of protected health information (PHI) using health insurance big data.
  • a U-health care unit that provides real-time u-healthcare utilizing data related to health information
  • a PHI monitor unit that provides monitoring of protected health information (PHI) using health insurance big data.
  • the Ministry of Disease Prevention which provides disease prevention programs through analysis of disease data
  • the Department of Health Services which provides health care services for diagnosis, treatment, and post-care using u-healthcare
  • Data management department that performs integrated data management that provides recommended services through existing personal health records, predictive model unit that provides health predictable models through health examination results, treatment and medication history analysis, and personal genetic information and health forms It may further include a disease analysis unit for analyzing the probability of disease occurrence through the integration of information.
  • the exercise management unit may further include an exercise manager that collects the user's physical information and exercise information, calculates an exercise score according to an individual exercise practice index formula, and calculates an exercise degree according to the calculated exercise score.
  • the apparatus may further include an absolute stress management unit that collects the stress information of the user, calculates an absolute stress score according to an individual absolute stress index formula, and calculates a degree of stress of the user according to the calculated absolute stress score.
  • the method may further include a relative stress management unit that collects the absolute stress score, calculates a relative stress score according to an individual relative stress index formula, and calculates a degree of stress of a user according to the calculated relative stress score.
  • a relative stress management unit that collects the absolute stress score, calculates a relative stress score according to an individual relative stress index formula, and calculates a degree of stress of a user according to the calculated relative stress score.
  • Health information prediction method for achieving the above another object, using the open personal health record platform corresponding to big data, deriving the medical examination data and diagnostic data and the health of each disease Calculating a score.
  • the method may further include calculating an individual health score based on the health examination data and the diagnosis data.
  • the method may further include calculating a future health score for each disease of the user.
  • the method may further include deriving an individual health index based on the individual health score and the future health score for each disease.
  • the method may further include comparing and analyzing an environment group similar to a user based on the disease-specific health score.
  • the method may further include deriving medical institution medical service evaluation data.
  • the health information prediction method according to the present invention may further comprise the step of building a personal health record platform.
  • the building of the personal health record platform may include collecting health and disease knowledge, feeding back a reference to a knowledge repository, using a personal health information record interface, using an API for service system integration, and linkage. Linking data with atypical data, providing cloud computing services using big data, and predicting personal health care through public data of personal health records.
  • the present invention also provides a step of providing a real-time eu healthcare using data related to health information, monitoring PHI using health insurance big data, providing a disease prevention program through disease data analysis, u healthcare Providing medical services for diagnosis, treatment, and follow-up, providing integrated data management that provides recommendation services through the timing of vaccination and existing personal health records by linking vaccination data, health examination
  • the method may further include providing a health predictable model through treatment and analysis of medication history, and analyzing a probability of disease occurrence through integration of individual genetic information and health form information.
  • the method may further include collecting physical information and exercise information of the user, calculating an exercise score according to an individual exercise practice index formula, and calculating a degree of exercise of the user according to the calculated exercise score.
  • the method may further include collecting stress information of the user, calculating an absolute stress score according to an individual absolute stress index formula, and calculating a degree of stress of the user according to the calculated absolute stress score.
  • the method may further include collecting the absolute stress score, calculating a relative stress score according to an individual relative stress index formula, and calculating a degree of stress of a user according to the calculated relative stress score.
  • 1 is a result graph of Google's flu trend service through a big data analysis technique.
  • FIG. 2 is a schematic diagram of an apparatus for predicting health information according to an embodiment of the present invention.
  • FIG. 3 is a block diagram of an apparatus for predicting health information according to an embodiment of the present invention.
  • FIG. 4 is a flowchart illustrating a method of calculating a health score for each disease according to the present invention.
  • 5 is an output screen of the individual health indicator derived by the health care unit according to an embodiment of the present invention.
  • FIG 6 is an operation flowchart of the exercise degree calculation method according to the present invention.
  • FIG. 7 is an operation flowchart of a method for calculating an absolute stress level according to the present invention.
  • FIG. 8 is a flowchart illustrating a method of calculating a relative stress level according to the present invention.
  • Health information prediction apparatus for achieving the above object, the medical examination unit for deriving the medical examination data, the diagnosis using the open personal health recording platform corresponding to big data, the diagnostic data
  • the health score calculator may calculate a health score for each department and a disease and an individual health score.
  • Health information prediction method for achieving the above another object, using the open personal health record platform corresponding to big data, deriving the medical examination data and diagnostic data and the health of each disease Calculating a score.
  • first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.
  • the first component may be referred to as the second component, and similarly, the second component may also be referred to as the first component.
  • the term “and / or” includes any combination of a plurality of related items or any of a plurality of related items.
  • 1 is a result graph of Google's flu trend service through a big data analysis technique.
  • Google a global company, has started trending services as more companies use big data directly or indirectly for their businesses. It's a big data-based service that charts Google's website search keyword trends in real time.
  • This trend service may be the flu trend service.
  • This service informs the world about the level and spread of flu risks by country around the world. It compares the number of flu-related search terms with the existing flu monitoring system and shows the flu trends around the world through big data analysis techniques.
  • FIG. 2 is a schematic diagram of an apparatus for predicting health information according to an embodiment of the present invention.
  • the block diagram according to the operation of the health information prediction apparatus according to an embodiment of the present invention is open personal health record platform 100, personal health record database 150, health information prediction device 300 and an external organization 500 It may include.
  • the open personal health record platform 100 may include a personal health record database 150, and the open personal health record platform 100 may be a third party or another person's personal health record platform.
  • the open personal health record platform 100 may be directly serviced by building a health and disease management knowledge database, in which case, the closed personal health record platform may be serviced.
  • the health and disease management knowledge database includes an information collection unit that collects health and disease management knowledge, a feedback unit that feeds back with reference to the knowledge repository, a personal health information record interface, and an application programming interface for integrating and linking service systems. , API), data linking unit linking structured data with unstructured data, cloud service unit providing cloud computing service using big data, and health prediction unit predicting individual health management through public data of personal health records. have.
  • the open personal health record platform 100 may collect personal health information from an external organization 500 and store the personal health information in the personal health record database 150.
  • the external institution 500 may include a hospital, a fitness center, a psychological counseling center, a company, and the personal health information may include examination data, diagnosis data, bio signals, physical information, exercise information, and stress information.
  • the health information predicting apparatus 300 may include an exercise manager 380, an absolute stress manager 385, and a relative stress manager 390.
  • the health information prediction device 300 monitors the protected health information (PHI) using the U-Health Care Department, which provides real-time u-healthcare using health information-related data, and health insurance big data.
  • PHI protected health information
  • U-Health Care Department which provides real-time u-healthcare using health information-related data, and health insurance big data.
  • Data management department that provides integrated data management that provides recommendation services through vaccination timing and existing personal health records, and predictive models and individuals that provide health predictable models through analysis of health examination results, treatment and medication history. It may include a disease analysis unit for analyzing the probability of disease occurrence through integration of genetic information and health type information.
  • the health information predicting apparatus 300 may be provided through the service of the open personal health record platform 100 including the personal health record database 150 or the personal health record platform including the personal health and disease management knowledge database. Personal health information can be derived.
  • the health information predicting device 300 may provide the user with information such as a health score, a personal health score, a degree of exercise, a stress level, etc. of each user acquired using personal health information, and provide the information to an external institution 500.
  • the external organization 500 may provide a customized service to the user.
  • FIG. 3 is a block diagram of an apparatus for predicting health information according to an embodiment of the present invention.
  • the apparatus 300 for predicting health information includes a health checker 310, a health checkup database (Data Base, DB) 315, a diagnosis part 320, and a diagnosis.
  • the medical service DB 375 may be included.
  • the health checker 310 may derive the user's health checkup data from the open personal health record platform 100.
  • the medical examination DB 315 may store the medical examination data and provide the user with the medical examination data.
  • the diagnosis unit 320 may derive the diagnosis data and the biosignal of the user from the open personal health recording platform 100, and the diagnosis DB 325 may store the diagnosis data and the biosignal and provide the same to the user. .
  • the health score calculator 330 may extract health examination data from the health examination DB 315, and extract diagnosis data from the diagnosis DB 325. In addition, the health score calculator 330 may extract the biosignal and information of the external organs from the open personal health record platform 100, and may calculate a health score and a personal health score for each disease based on the information.
  • the personal health score may be calculated by a personal health score calculation formula consisting of a plurality of disease-specific health scores.
  • the personal health score calculation formula may include a sum of disease scores for each disease, an average equation of health scores for each disease, or an equation divided by the number of diseases after adding the weights for each disease.
  • the future health score calculator 340 may extract the health examination data, the diagnosis data, the biosignal, and the information of the external organ from the health score calculator 330, and may calculate the future health score for each disease based on this. .
  • the health score DB 335 may store a disease-specific health score and an individual health score from the health score calculator 330 and provide the user with the health score. In addition, the health score DB 335 may store the future health score for each disease from the future health score calculator 340, and may provide it to the user.
  • the health management unit 350 may extract a health score for each disease, a personal health score, and a future health score for each disease from the health score DB 335, and derive an individual health index based on the health score and provide the user with the health score.
  • the comparison analyzer 360 may extract an individual health score from the health score DB 335, and provide the user with a comparative analysis with an environment group similar to the user.
  • the service evaluation unit 370 may derive the medical institution medical service evaluation information from the open personal health record platform 100.
  • the medical institution medical service DB 375 may store the medical institution medical service evaluation information and provide the user.
  • FIG. 4 is an operation flowchart of a method for calculating a health score for each disease according to the present invention.
  • the health score calculation method of FIG. 4 may be performed by the health score calculation unit 330 shown in FIG. 3, but the operation subject is not limited thereto.
  • the method for calculating a health score for each disease derives health examination data from an open personal health record platform (S410), and also derives diagnosis data (S420).
  • a score determining factor for calculating a health score for each disease is calculated (S430), and an individual health score for each disease is calculated based on the score determining factor based on the health examination data and the diagnosis data (S440).
  • health check data of a user related to hypertension is derived from an open personal health record platform (S410), and diagnostic data related to hypertension is derived (S420).
  • the score determinants for calculating the health score for hypertension are referred to the big data of the open personal health record platform, as shown in Table 1, as shown in Table 1 for gender, age, income category, family history of hypertension, family history of cancer, BMI, daily smoking amount and The amount of alcohol is calculated (S430).
  • the data is matched according to the score determining factor as shown in Table 2 through the medical examination data of the user related to hypertension and the diagnostic data related to the hypertension.
  • the health score for each disease may be calculated through Equation 1.
  • Equation 1 ⁇ i is a weight for the score determining factor, X i is a score determining factor.
  • a health score for hypertension is calculated as shown in Equation 2 (S440).
  • the health information prediction apparatus may calculate a score by applying to various diseases such as diabetes and obesity, in addition to the above-described high blood pressure.
  • 5 is an output screen of the individual health indicator derived by the health care unit according to an embodiment of the present invention.
  • the personalized health indicator of the present invention may provide a future 10-year forecast graph, a personalized exercise, and a future predicted graph when performing the exercise based on a health score for each disease and a future health score for each disease.
  • the items provided by the personalized health indicator of the present invention may include a personal health score, a future health score for each disease, health examination data, diagnostic data, the same group comparison analysis results and medical institution medical service evaluation information.
  • the items provided by the personalized health indicator of the present invention are not limited to the above items.
  • FIG 6 is an operation flowchart of the exercise degree calculation method according to the present invention.
  • the health information predicting apparatus 300 may include an exercise manager 380 that determines a degree of exercise based on the user's body information and exercise information.
  • the exercise degree calculation method illustrated in FIG. 6 may be performed by the exercise manager 380 illustrated in FIG. 2, but the operation subject is not limited thereto.
  • the method of calculating the exercise degree of the exercise management unit 380 collects the user's physical information and exercise information from the open personal health record platform 100 or the personal health record platform directly serving (S610), and designated as shown in Equation 3
  • the exercise score is calculated according to the equation (S620). Determine whether the exercise score is 100 or less (S630), if less than 100 is calculated as lack of exercise (S640), if more than 100 is calculated as over exercise (S650).
  • Equation 4 E p in Equation 3 may be calculated in Equation 4.
  • ⁇ 1 , ⁇ 2 , ⁇ 3, and ⁇ 4 are values that are measured differently according to each user, b is weight, m is muscle strength, q is body fat, and l is physical activity level. p stands for personal number, s stands for current state, and r stands for recommended state.
  • Equation 4 q may be calculated by Equation 5, and l may be calculated by Equation 6.
  • Equation 5 ⁇ 1 and ⁇ 2 are values measured differently according to each user, q 1 means body fat weight and q 2 means muscle weight.
  • Equation 6 y 1 , y 2 and y 3 is a value measured differently according to each user, l 1 is the amount of work, l 2 is the lung capacity and l 3 is the respiratory capacity.
  • the exercise manager 380 may track and manage the amount of exercise of the user by calculating the exercise score and the degree of exercise, and provide the exercise score and the degree of exercise to the user.
  • the exercise manager 380 may provide an exercise score and a degree of exercise to the external institution 500 in order for the user to receive a personalized exercise service and a fitness management service from the external institution 500.
  • the equation for calculating the exercise score may be referred to as a personal exercise activity index (PEAI), but is not limited to Equation 3.
  • PEAI personal exercise activity index
  • FIG. 7 is an operation flowchart of a method for calculating an absolute stress level according to the present invention.
  • the health information predicting apparatus 300 may include an absolute stress management unit 385 that determines an absolute stress level based on the stress information of the user. 7 may be performed by the absolute stress management unit 385 shown in FIG. 2, but the operation subject is not limited thereto.
  • the method of estimating the stress level of the absolute stress management unit 385 collects the user's stress information from the open personal health record platform 100 or the personal health record platform serving directly (S710), According to the calculation of the absolute stress score (S720). It is determined whether the absolute stress score is 100 or less (S730), and if it is 100 or less, it is calculated as a stress stability (S740), and if it is more than 100, it is calculated as an excessive stress (S750).
  • AS p in Equation 7 may be calculated by Equation 8.
  • ⁇ p is a value measured differently according to each user
  • hr n is a heart rate in the nth data
  • hr s is a heart rate in a stable situation
  • p is an individual number.
  • Absolute stress management unit 385 by calculating the absolute stress score and the degree of stress, it is possible to manage the work environment and mental health management by job and department of the user, and provide the absolute stress score and stress degree to the user can do.
  • the absolute stress management unit 385 may provide the absolute stress score and the degree of stress to the external organization 500 in order for the user to receive mental care services from the external organization 500.
  • the equation for calculating the absolute stress score may be referred to as a personal absolute stress index (PASI), and is not limited to Equation 7.
  • PASI personal absolute stress index
  • FIG. 8 is a flowchart illustrating a method of calculating a relative stress level according to the present invention.
  • the health information predicting apparatus 300 may include a relative stress manager 390 for determining a relative stress level based on an absolute stress score. 8 may be performed by the relative stress management unit 390 shown in FIG. 2, but an operation subject is not limited thereto.
  • the method of calculating the stress level of the relative stress management unit 390 collects the absolute stress score of the user from the absolute stress management unit 385 (S810), and calculates the relative stress score according to the specified equation as shown in Equation 9 (S820). ). It is determined whether the relative stress score is 100 or less (S830), and if it is 100 or less, it is calculated that the workplace stress is below the average (S840), and if it is more than 100, it is calculated that the workplace stress is above the average (S850).
  • Equation 10 RS p in Equation 9 may be calculated through Equation 10.
  • AS is an absolute stress score
  • p is an individual number
  • n is the total number of people in the company.
  • Relative stress management unit 390 by calculating the relative stress score and the degree of stress, can manage the work environment and mental health management by job and department of the user, and provide the relative stress score and the degree of stress to the user can do.
  • the relative stress management unit 390 may provide the relative stress score and the degree of stress to the external organization 500 in order for the user to receive mental care services from the external organization 500.
  • the equation for calculating the relative stress score may be referred to as a personal relative stress index (PRSI), and is not limited to Equation 9.
  • PRSI personal relative stress index
  • Computer-readable recording media include all kinds of recording devices that store data that can be read by a computer system.
  • the computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable program or code is stored and executed in a distributed fashion.
  • the computer-readable recording medium may include a hardware device specifically configured to store and execute program instructions, such as a ROM, a RAM, a flash memory, and the like.
  • Program instructions may include high-level language code that can be executed by a computer using an interpreter, as well as machine code such as produced by a compiler.
  • While some aspects of the invention have been described in the context of a device, it may also represent a description according to a corresponding method, wherein the block or device corresponds to a method step or a feature of the method step. Similarly, aspects described in the context of a method may also be indicated by the features of the corresponding block or item or corresponding device.
  • Some or all of the method steps may be performed by (or using) a hardware device such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, one or more of the most important method steps may be performed by such an apparatus.
  • a programmable logic device eg, a field programmable gate array
  • the field programmable gate array may operate in conjunction with a microprocessor to perform one of the methods described herein.
  • the methods are preferably performed by any hardware apparatus.

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Abstract

La présente invention concerne un dispositif de prédiction d'informations de santé qui calcule un score de santé spécifique à une maladie par le biais d'une plateforme d'enregistrements de santé personnelle de type ouvert correspondant à des mégadonnées, qui réalise une comparaison et une analyse de groupes environnementaux similaires à un utilisateur, et qui fournit des scores d'exercices et de stress, des informations d'estimation de service médical d'un établissement médical et un indicateur de santé individuel. Grâce au dispositif de prédiction d'informations de santé selon la présente invention, un utilisateur peut s'occuper lui-même de sa santé et améliorer sa qualité de vie.
PCT/KR2017/014159 2016-12-06 2017-12-05 Dispositif et procédé de prédiction d'informations de santé à l'aide de mégadonnées Ceased WO2018105995A2 (fr)

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CN109659001A (zh) * 2018-12-18 2019-04-19 延安大学 一种防癌监管系统及方法
CN109887566A (zh) * 2019-02-26 2019-06-14 卫宁健康科技集团股份有限公司 电子健康档案的智能管理方法及系统
CN110033866A (zh) * 2019-03-08 2019-07-19 平安科技（深圳）有限公司 健康提醒方法、装置、计算机设备及存储介质
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