WO2018105995A2 - Device and method for health information prediction using big data - Google Patents

Device and method for health information prediction using big data Download PDF

Info

Publication number
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
Authority
WO
WIPO (PCT)
Prior art keywords
health
score
information
disease
data
Prior art date
Application number
PCT/KR2017/014159
Other languages
French (fr)
Korean (ko)
Other versions
WO2018105995A3 (en
Inventor
이대호
Original Assignee
주식회사 원소프트다임
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from KR1020170016988A external-priority patent/KR101970947B1/en
Application filed by 주식회사 원소프트다임 filed Critical 주식회사 원소프트다임
Priority to US16/465,543 priority Critical patent/US20200005944A1/en
Publication of WO2018105995A2 publication Critical patent/WO2018105995A2/en
Publication of WO2018105995A3 publication Critical patent/WO2018105995A3/en

Links

Images

Classifications

    • 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.

Abstract

Disclosed is a health information prediction device which calculates a disease-specific health score through an open-type personal health record platform corresponding to big data, performs comparison and analysis of environmental groups similar to a user, and provides exercise and stress scores, medical service assessment information of a medical institution, and an individual health indicator. By the health information prediction device of the present invention, a user can expect self-health care and life improvement.

Description

빅데이터를 활용한 건강정보 예측 장치 및 방법Apparatus and method for predicting health information using big data
본 발명은 빅데이터를 활용한 건강정보 예측 장치 및 방법에 관한 것으로, 보다 상세하게는 빅데이터를 활용하여 자가 건강 관리를 제공하는 예측 장치 및 방법에 관한 것이다.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.
빅데이터는 기존의 관계형 데이터베이스의 처리 수준을 뛰어넘는 엄청난 양의 데이터를 빠른 속도로 효과적으로 수집·분석 하는 기술로, 주요국 및 글로벌 기업은 빅데이터 산업의 육성 및 활용에 주력하고 있으며, 이미 30% 정도의 기업이 빅데이터를 직간접적으로 비즈니스에 활용하고 있으며, 그 비율은 점차 증가할 것으로 예상된다.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.
미국 국립보건원의 경우, 빅데이터를 활용한 Pillbox 프로젝트를 통한 의료 개혁을 시도하고 있는데, 일 예로 국립의학도서관(National Library of Medicine)이 운영하는 약 검색 사이트 등을 들 수 있다. 해당 사이트는 약 검색 서비스를 통해 얻어진 다양한 사용자 질병에 대한 통계 데이터를 활용하여, 주요 관리 대상에 해당하는 질병에 대한 관리 및 예측이 가능하며, 질병의 분포 및 추세를 예측함으로써 국가차원의 조기 대응이 가능하다.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. By using statistical data on various user diseases obtained through drug search service, the site can manage and predict diseases that are the main targets of management. It is possible.
국내에서도 고령화 및 만성질환의 문제가 급증하고 있으며, 사후 치료에서 예방적 의료서비스로 추세가 전환되고 있다. 이에 따라 건강 및 질병에 대한 관리 및 예측에 관심이 높아지게 되어, 미국 국립보건원의 약 검색 사이트와 같은 서비스의 수요가 꾸준히 증가하고 있는 실정이다.Aging and chronic diseases are rapidly increasing in Korea, and the trend is shifting from post-treatment to preventive care. Accordingly, the interest in the management and prediction of health and disease is increasing, and the demand for services such as the US National Institute of Health's drug search site is steadily increasing.
상기와 같은 문제점을 해결하기 위한 본 발명의 목적은 빅데이터를 통해 건강정보를 예측하는 장치를 제공하는 데 있다.An object of the present invention for solving the above problems is to provide an apparatus for predicting health information through big data.
상기와 같은 문제점을 해결하기 위한 본 발명의 목적은 빅데이터를 통해 건강정보를 예측하는 방법을 제공하는 데 있다.An object of the present invention for solving the above problems is to provide a method for predicting health information through big data.
상기 목적을 달성하기 위한 본 발명의 일 실시예에 따른 건강정보 예측 장치는, 빅데이터에 해당하는 개방형 개인 건강 기록 플랫폼을 이용하여, 건강검진 데이터를 도출하는 건강검진부, 진단 데이터를 도출하는 진단부 및 질환별 건강 점수와 개인 건강 점수를 산출하는 건강점수 산출부를 포함할 수 있다.Health information prediction apparatus according to an embodiment of the present invention 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.
여기서, 사용자의 질환별 미래 건강 점수 산출하는 미래건강점수 산출부를 더 포함할 수 있다.The apparatus may further include a future health score calculator configured to calculate a future health score for each disease of the user.
또한, 상기 개인 건강 점수와 상기 질환별 미래 건강 점수를 기반으로 개인별 건강 지표를 도출하는 건강관리부를 더 포함할 수 있다.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.
추가적으로, 상기 질환별 건강 점수를 기반으로 사용자와 비슷한 환경 집단을 비교 분석하는 비교분석부를 더 포함할 수 있다.Additionally, 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.
그리고, 의료기관의 의료서비스 평가 정보를 도출하는 서비스평가부를 더 포함할 수 있다.The apparatus may further include a service evaluation unit for deriving medical service evaluation information of a medical institution.
한편, 본 발명에 따른 건강정보 예측 장치는, 개인 건강기록 플랫폼을 더 포함할 수 있다.On the other hand, the health information prediction apparatus according to the present invention may further include a personal health record platform.
상기 개인 건강기록 플랫폼은 건강·질병관리 지식을 수집하는 정보수집부, 지식 저장소를 참조하여 피드백하는 피드백부, 개인생활건강정보기록 인터페이스, 서비스 통합 및 연계용 오플리케이션 프로그래밍 인터페이스, 정형데이터와 비정형데이터를 연계하는 데이터 연계부, 빅데이터를 활용한 클라우드 컴퓨팅 서비스를 제공하는 클라우드 서비스부 및 개인 건강 기록의 공공데이터화를 통한 개인별 건강관리를 예측하는 건강예측부를 포함할 수 있다.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.
본 발명은 또한, 건강정보 관련 데이터를 활용한 실시간 유헬스케어를 제공하는 유헬스케어부, 건강보험 빅데이터를 활용한 보호되는 건강 정보(protected health information, PHI)의 모니터링을 제공하는 PHI모니터부, 질병 데이터 분석을 통한 질병 예방 프로그램을 제공하는 질병예방부, 유헬스케어를 활용한 진단, 치료, 사후관리의 보건의료서비스를 제공하는 의료서비스부, 예방접종 데이터 연계를 연계시켜 예방접종 시기 및 기존 개인 건강 기록 자료를 통해 추천서비스를 제공하는 통합적인 데이터 관리를 수행하는 데이터관리부, 건강검진 결과, 진료 및 투약내역 분석을 통한 건강예측 가능 모형을 제공하는 예측모형부 및 개인 유전자정보와 건강형태 정보 통합을 통한 질병발생확률을 분석하는 질병분석부를 더 포함할 수 있다.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. , 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.
여기서, 사용자의 신체 정보 및 운동 정보를 수집하고, 개인별 운동 실천 지표 공식에 따라 운동 점수를 산출하고, 산출한 상기 운동 점수에 따라 운동 정도를 산정하는 운동 관리부를 더 포함할 수 있다.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.
또한, 사용자의 스트레스 정보를 수집하고, 개인별 절대 스트레스 지표 공식에 따른 절대 스트레스 점수를 산출하고, 산출한 상기 절대 스트레스 점수에 따라 사용자의 스트레스 정도를 산정하는 절대 스트레스 관리부를 더 포함할 수 있다.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.
추가적으로, 상기 절대 스트레스 점수를 수집하고, 개인별 상대 스트레스 지표 공식에 따라 상대 스트레스 점수를 산출하고, 산출한 상기 상대 스트레스 점수에 따라 사용자의 스트레스 정도를 산정하는 상대 스트레스 관리부를 더 포함할 수 있다.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.
상기 다른 목적을 달성하기 위한 본 발명의 일 실시예에 따른 건강정보 예측 방법은, 빅데이터에 해당하는 개방형 개인 건강 기록 플랫폼을 이용하여, 건강검진 데이터 및 진단 데이터를 도출하는 단계 및 각 질환별 건강 점수를 산출하는 단계를 포함할 수 있다.Health information prediction method according to an embodiment of the present invention 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.
여기서, 상기 건강검진 데이터 및 진단 데이터를 기반으로 개인 건강 점수를 산출하는 단계를 더 포함할 수 있다.The method may further include calculating an individual health score based on the health examination data and the diagnosis data.
또한, 사용자의 질환별 미래 건강 점수를 산출하는 단계를 더 포함할 수 있다.The method may further include calculating a future health score for each disease of the user.
추가적으로, 상기 개인 건강 점수와 상기 질환별 미래 건강 점수를 기반으로 개인별 건강 지표를 도출하는 단계를 더 포함할 수 있다.Additionally, the method may further include deriving an individual health index based on the individual health score and the future health score for each disease.
그리고, 상기 질환별 건강 점수를 기반으로 사용자와 비슷한 환경 집단을 비교 분석하는 단계를 더 포함할 수 있다.The method may further include comparing and analyzing an environment group similar to a user based on the disease-specific health score.
여기서, 의료기관 의료서비스 평가 데이터를 도출하는 단계를 더 포함할 수 있다.Here, the method may further include deriving medical institution medical service evaluation data.
한편, 본 발명에 따른 건강정보 예측 방법은, 개인 건강 기록 플랫폼을 구축하는 단계를 더 포함할 수 있다. On the other hand, the health information prediction method according to the present invention may further comprise the step of building a personal health record platform.
상기 개인 건강 기록 플랫폼을 구축하는 단계는 건강·질병 지식을 수집하는 단계, 지식저장소를 참조하여 피드백하는 단계, 개인생활건강정보기록 인터페이스를 이용하는 단계, 서비스 시스템 통합 및 연계용 API를 이용하는 단계, 정형데이터와 비정형데이터를 연계하는 단계, 빅데이터를 활용한 클라우드 컴퓨팅 서비스를 제공하는 단계 및 개인 건강 기록의 공공데이터화를 통한 개인별 건강관리를 예측하는 단계를 포함할 수 있다.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.
본 발명은 또한, 건강정보 관련 데이터를 활용한 실시간 유헬스케어를 제공하는 단계, 건강보험 빅데이터를 활용한 PHI를 모니터링하는 단계, 질병 데이터 분석을 통한 질병 예방 프로그램를 제공하는 단계, 유헬스케어를 활용한 진단, 치료, 사후관리의 보건의료서비스를 제공하는 단계, 예방접종 데이터를 연계시켜 예방접종 시기 및 기존 개인 건강 기록 자료를 통해 추천서비스를 제공하는 통합적인 데이터 관리를 제공하는 단계, 건강검진 결과, 진료 및 투약내역 분석을 통한 건강예측 가능 모형을 제공하는 단계 및 개인 유전자정보와 건강형태 정보 통합을 통한 질병발생확률 분석하는 단계를 더 포함할 수 있다.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 As a result, 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.
여기서, 사용자의 신체 정보 및 운동 정보를 수집하는 단계, 개인별 운동 실천 지표 공식에 따라 운동 점수를 산출하는 단계 및 산출한 상기 운동 점수에 따라 사용자의 운동 정도를 산정하는 단계를 더 포함할 수 있다.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.
또한, 사용자의 스트레스 정보를 수집하는 단계, 개인별 절대 스트레스 지표 공식에 따라 절대 스트레스 점수를 산출하는 단계 및 산출한 상기 절대 스트레스 점수에 따라 사용자의 스트레스 정도를 산정하는 단계를 더 포함할 수 있다.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.
추가적으로, 상기 절대 스트레스 점수를 수집하는 단계, 개인별 상대 스트레스 지표 공식에 따라 상대 스트레스 점수를 산출하는 단계 및 산출한 상기 상대 스트레스 점수에 따라 사용자의 스트레스 정도를 산정하는 단계를 더 포함할 수 있다.Additionally, 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.
본 발명에 따르면, 개인 건강정보를 관리함으로써 개인별 건강 지표를 제공하여, 자가 건강 관리에 활용될 수 있다.According to the present invention, by providing personal health indicators by managing personal health information, it can be utilized for self-health management.
본 발명에 따르면, 의료기관의 서비스 평가 정보를 제공하여 의료기관 선택시에 활용될 수 있다.According to the present invention, by providing a service evaluation information of the medical institution can be utilized when selecting a medical institution.
또한, 본 발명에 따르면, 바쁜 직장인의 운동 및 스트레스를 관리함으로써 근로자의 생활 개선을 기대할 수 있다.In addition, according to the present invention, by improving the exercise and stress of busy office workers can be expected to improve the life of the worker.
도 1은 빅데이터 분석 기법을 통한 구글의 독감 트렌드 서비스의 결과 그래프이다.1 is a result graph of Google's flu trend service through a big data analysis technique.
도 2는 본 발명의 일 실시예에 따른 건강정보 예측 장치의 개요도이다.2 is a schematic diagram of an apparatus for predicting health information according to an embodiment of the present invention.
도 3은 본 발명의 일 실시예에 따른 건강정보 예측 장치의 블록 구성도이다.3 is a block diagram of an apparatus for predicting health information according to an embodiment of the present invention.
도 4는 본 발명에 따른 질환별 건강 점수 산출 방법의 동작 순서도이다.4 is a flowchart illustrating a method of calculating a health score for each disease according to the present invention.
도 5는 본 발명의 일 실시예에 따른 건강관리부가 도출한 개인별 건강 지표의 출력 화면이다.5 is an output screen of the individual health indicator derived by the health care unit according to an embodiment of the present invention.
도 6은 본 발명에 따른 운동 정도 산정 방법의 동작 순서도이다.6 is an operation flowchart of the exercise degree calculation method according to the present invention.
도 7은 본 발명에 따른 절대 스트레스 정도 산정 방법의 동작 순서도이다.7 is an operation flowchart of a method for calculating an absolute stress level according to the present invention.
도 8은 본 발명에 따른 상대 스트레스 정도 산정 방법의 동작 순서도이다.8 is a flowchart illustrating a method of calculating a relative stress level according to the present invention.
상기 목적을 달성하기 위한 본 발명의 일 실시예에 따른 건강정보 예측 장치는, 빅데이터에 해당하는 개방형 개인 건강 기록 플랫폼을 이용하여, 건강검진 데이터를 도출하는 건강검진부, 진단 데이터를 도출하는 진단부 및 질환별 건강 점수와 개인 건강 점수를 산출하는 건강점수 산출부를 포함할 수 있다.Health information prediction apparatus according to an embodiment of the present invention 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.
상기 다른 목적을 달성하기 위한 본 발명의 일 실시예에 따른 건강정보 예측 방법은, 빅데이터에 해당하는 개방형 개인 건강 기록 플랫폼을 이용하여, 건강검진 데이터 및 진단 데이터를 도출하는 단계 및 각 질환별 건강 점수를 산출하는 단계를 포함할 수 있다.Health information prediction method according to an embodiment of the present invention 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.
본 발명은 다양한 변경을 가할 수 있고 여러 가지 실시예를 가질 수 있는 바, 특정 실시예들을 도면에 예시하고 상세한 설명에 상세하게 설명하고자 한다. 그러나, 이는 본 발명을 특정한 실시 형태에 대해 한정하려는 것이 아니며, 본 발명의 사상 및 기술 범위에 포함되는 모든 변경, 균등물 내지 대체물을 포함하는 것으로 이해되어야 한다. 각 도면을 설명하면서 유사한 참조부호를 유사한 구성요소에 대해 사용하였다. As the invention allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the written description. However, this is not intended to limit the present invention to specific embodiments, it should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. In describing the drawings, similar reference numerals are used for similar elements.
제1, 제2, A, B 등의 용어는 다양한 구성요소들을 설명하는데 사용될 수 있지만, 상기 구성요소들은 상기 용어들에 의해 한정되어서는 안 된다. 상기 용어들은 하나의 구성요소를 다른 구성요소로부터 구별하는 목적으로만 사용된다. 예를 들어, 본 발명의 권리 범위를 벗어나지 않으면서 제1 구성요소는 제2 구성요소로 명명될 수 있고, 유사하게 제2 구성요소도 제1 구성요소로 명명될 수 있다. "및/또는"이라는 용어는 복수의 관련된 기재된 항목들의 조합 또는 복수의 관련된 기재된 항목들 중의 어느 항목을 포함한다. Terms such as 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. For example, without departing from the scope of the present invention, 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.
어떤 구성요소가 다른 구성요소에 "연결되어" 있다거나 "접속되어" 있다고 언급된 때에는, 그 다른 구성요소에 직접적으로 연결되어 있거나 또는 접속되어 있을 수도 있지만, 중간에 다른 구성요소가 존재할 수도 있다고 이해되어야 할 것이다. 반면에, 어떤 구성요소가 다른 구성요소에 "직접 연결되어" 있다거나 "직접 접속되어" 있다고 언급된 때에는, 중간에 다른 구성요소가 존재하지 않는 것으로 이해되어야 할 것이다. When a component is referred to as being "connected" or "connected" to another component, it may be directly connected to or connected to that other component, but it may be understood that other components may be present in between. Should be. On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it should be understood that there is no other component in between.
본 출원에서 사용한 용어는 단지 특정한 실시예를 설명하기 위해 사용된 것으로, 본 발명을 한정하려는 의도가 아니다. 단수의 표현은 문맥상 명백하게 다르게 뜻하지 않는 한, 복수의 표현을 포함한다. 본 출원에서, "포함하다" 또는 "가지다" 등의 용어는 명세서상에 기재된 특징, 숫자, 단계, 동작, 구성요소, 부품 또는 이들을 조합한 것이 존재함을 지정하려는 것이지, 하나 또는 그 이상의 다른 특징들이나 숫자, 단계, 동작, 구성요소, 부품 또는 이들을 조합한 것들의 존재 또는 부가 가능성을 미리 배제하지 않는 것으로 이해되어야 한다.The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms "comprise" or "have" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.
다르게 정의되지 않는 한, 기술적이거나 과학적인 용어를 포함해서 여기서 사용되는 모든 용어들은 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자에 의해 일반적으로 이해되는 것과 동일한 의미를 가지고 있다. 일반적으로 사용되는 사전에 정의되어 있는 것과 같은 용어들은 관련 기술의 문맥 상 가지는 의미와 일치하는 의미를 가지는 것으로 해석되어야 하며, 본 출원에서 명백하게 정의하지 않는 한, 이상적이거나 과도하게 형식적인 의미로 해석되지 않는다.Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in the commonly used dictionaries should be construed as having meanings consistent with the meanings in the context of the related art and shall not be construed in ideal or excessively formal meanings unless expressly defined in this application. Do not.
이하, 본 발명에 따른 바람직한 실시예를 첨부된 도면을 참조하여 상세하게 설명한다.Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.
도 1은 빅데이터 분석 기법을 통한 구글의 독감 트렌드 서비스의 결과 그래프이다.1 is a result graph of Google's flu trend service through a big data analysis technique.
글로벌 기업인 구글(Google)은 빅데이터를 직간접적으로 비즈니스에 이용하는 기업이 증가함에 따라 트렌드 서비스를 시작하였다. 이는 구글의 웹사이트 검색 키워드 추세를 도표화하여 실시간으로 보여주는 빅데이터 기반의 서비스이다.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.
이 트렌드 서비스의 일 예로 독감 트렌드 서비스를 언급할 수 있다. 이 서비스는 전 세계의 국가별 독감 위험 수준과 확산 현황을 알려주는 서비스로, 독감 관련 검색어 수를 기존의 독감 감시 시스템과 비교하여 빅데이터 분석 기법을 통해 전 세계의 독감 추이를 보여준다.An example of 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.
도 1은 독감 트렌드 서비스가 스위스의 독감 유행 수준을 예측한 결과 그래프이다. 파란선은 구글의 독감 트렌드 예상치를 나타내며, 노란선은 스위스의 데이터를 나타낸다. 도 1에 따르면, 독감 트렌드 예상치는 실제 스위스 데이터와 유사하다고 볼 수 있다. 이 결과는 빅데이터를 통한 예측이 높은 정확성을 가진다는 점을 말해준다.1 is a graph of the flu trend service predicting the flu epidemic level in Switzerland. The blue line represents Google's flu trend estimates, while the yellow line represents Switzerland's data. According to Figure 1, the flu trend estimates are similar to the actual Swiss data. This result indicates that the prediction through big data has high accuracy.
도 2는 본 발명의 일 실시예에 따른 건강정보 예측 장치의 개요도이다.2 is a schematic diagram of an apparatus for predicting health information according to an embodiment of the present invention.
본 발명의 일 실시예에 따른 건강정보 예측 장치의 동작에 따른 블록 구성도는 개방형 개인 건강 기록 플랫폼(100), 개인 건강 기록 데이터베이스(150), 건강정보 예측 장치(300) 및 외부기관(500)을 포함할 수 있다.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.
개방형 개인 건강 기록 플랫폼(100)은 개인 건강 기록 데이터베이스(150)를 포함할 수 있고, 개방형 개인 건강 기록 플랫폼(100)은 타사 또는 타기관의 개인 건강 기록 플랫폼일 수 있다. 또한, 개방형 개인 건강 기록 플랫폼(100)은 직접 건강·질병관리 지식 데이터베이스를 구축하여 서비스될 수 있고, 이 경우, 패쇄형 개인 건강 기록 플랫폼이 서비스 될 수 있다.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. In addition, 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.
건강·질병관리 지식 데이터베이스는, 건강·질병관리 지식을 수집하는 정보수집부, 지식 저장소를 참조하여 피드백하는 피드백부, 개인생활건강정보기록 인터페이스, 서비스 시스템 통합 및 연계용 어플리케이션 프로그래밍 인터페이스(Application Programming Interface, API), 정형데이터와 비정형데이터를 연계하는 데이터연계부, 빅데이터를 활용한 클라우드 컴퓨팅 서비스 제공하는 클라우드서비스부 및 개인 건강 기록의 공공데이터화를 통한 개인별 건강관리를 예측하는 건강예측부를 포함할 수 있다.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.
개방형 개인 건강 기록 플랫폼(100)은 외부기관(500)으로부터 개인 건강 정보를 수집할 수 있고, 개인 건강 정보를 개인 건강 기록 데이터베이스(150)에 저장할 수 있다. 외부기관(500)은 병원, 피트니스 센터, 심리상담 센터, 회사를 포함할 수 있고, 개인 건강 정보는 검진 데이터, 진단 데이터, 생체 신호, 신체 정보, 운동 정보 및 스트레스 정보를 포함할 수 있다.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.
건강정보 예측 장치(300)는 운동관리부(380), 절대 스트레스 관리부(385) 및 상대 스트레스 관리부(390)를 포함할 수 있다.The health information predicting apparatus 300 may include an exercise manager 380, an absolute stress manager 385, and a relative stress manager 390.
또한, 건강정보 예측 장치(300)는 건강정보 관련 데이터를 활용한 실시간 유헬스케어를 제공하는 유헬스케어부, 건강보험 빅데이터를 활용한 보호되는 건강 정보(Protected Health Information, PHI)의 모니터링을 제공하는 PHI모니터부, 질병 데이터 분석을 통한 질병 예방 프로그램을 제공하는 질병예방부, 유헬스케어를 활용한 진단, 치료, 사후관리의 보건의료서비스를 제공하는 의료서비스부, 예방접종 데이터 연계를 연계시켜 예방접종 시기 및 기존 개인 건강 기록 자료를 통해 추천서비스를 제공하는 통합적인 데이터 관리를 수행하는 데이터관리부, 건강검진 결과, 진료 및 투약내역 분석을 통한 건강예측 가능 모형을 제공하는 예측모형부 및 개인 유전자정보와 건강형태 정보 통합을 통한 질병발생확률을 분석하는 질병분석부를 포함할 수 있다.In addition, 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. Linking PHI monitor department, disease prevention department providing disease prevention program through disease data analysis, medical service department providing health care services for diagnosis, treatment and follow-up using u-healthcare, and immunization data connection 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.
건강정보 예측 장치(300)는 개인 건강 기록 데이터 베이스(150)를 포함하는 개방형 개인 건강 기록 플랫폼(100) 또는 직접 구축한 건강·질병관리 지식 데이터베이스를 포함하는 개인 건강 기록 플랫폼의 서비스를 통해, 상기 개인 건강 정보를 도출할 수 있다.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.
건강정보 예측 장치(300)는 개인 건강 정보를 이용하여 획득한 사용자의 질환별 건강 점수, 개인 건강 점수, 운동 정도, 스트레스 정도 등의 정보를 사용자에게 제공할 수 있고, 외부기관(500)에 제공하여 외부기관(500)이 사용자에게 맞춤 서비스를 제공하도록 할 수 있다. 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.
도 3는 본 발명의 일 실시예에 따른 건강정보 예측 장치의 블록 구성도이다.3 is a block diagram of an apparatus for predicting health information according to an embodiment of the present invention.
도 3에 도시된 바와 같이 본 발명의 일 실시예에 따른 건강정보 예측 장치(300)는 건강검진부(310), 건강검진 데이터베이스(Data Base, DB)(315), 진단부(320), 진단 DB(325), 건강점수 산출부(330), 미래건강점수 산출부(340), 건강점수 DB(335), 건강관리부(350), 비교분석부(360), 서비스평가부(370) 및 의료기관 의료서비스 DB(375)를 포함할 수 있다.As shown in FIG. 3, the apparatus 300 for predicting health information according to an embodiment of the present invention includes a health checker 310, a health checkup database (Data Base, DB) 315, a diagnosis part 320, and a diagnosis. DB 325, health score calculator 330, future health score calculator 340, health score DB 335, health care unit 350, comparative analysis unit 360, service evaluation unit 370 and medical institutions The medical service DB 375 may be included.
건강검진부(310)는 개방형 개인 건강 기록 플랫폼(100)으로부터 사용자의 건강검진 데이터를 도출할 수 있다. 건강검진 DB(315)는 건강검진 데이터를 저장할 수 있고, 사용자에게 제공할 수 있다.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.
진단부(320)는 개방형 개인 건강 기록 플랫폼(100)으로부터 사용자의 진단 데이터 및 생체신호를 도출할 수 있다, 진단 DB(325)는 진단 데이터 및 생체신호를 저장할 수 있고, 사용자에게 제공할 수 있다.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. .
건강점수 산출부(330)는 건강검진 DB(315)로부터 건강검진 데이터를 추출할 수 있고, 진단 DB(325)로부터 진단 데이터를 추출할 수 있다. 또한, 건강점수 산출부(330)는 개방형 개인 건강 기록 플랫폼(100)으로부터 생체 신호 및 외부기관의 정보를 추출할 수 있고, 이를 기반으로 질환별 건강 점수 및 개인 건강 점수를 산출할 수 있다.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.
개인 건강 점수는 복수의 질환별 건강 점수들로 이루어진 개인 건강 점수 계산식에 의해 산출될 수 있다. 개인 건강 점수 계산식은 질환별 건강 점수들의 합산식, 질환별 건강 점수들의 평균식 또는 질환별로 가중치를 다르게 주어 합산 후 질환의 개수로 나누는 식 등을 사용할 수 있다.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.
미래건강점수 산출부(340)는 건강점수 산출부(330)로부터 건강검진 데이터, 진단 데이터, 생체 신호 및 외부기관의 정보를 추출할 수 있고, 이를 기반으로 질환별 미래 건강 점수를 산출할 수 있다.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. .
건강점수 DB(335)는 건강점수 산출부(330)로부터 질환별 건강 점수 및 개인 건강 점수를 저장할 수 있고, 사용자에게 제공할 수 있다. 또한, 건강점수 DB(335)는 미래건강점수 산출부(340)로부터 질환별 미래 건강 점수를 저장할 수 있고, 사용자에게 제공할 수 있다.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.
건강관리부(350)는 건강점수 DB(335)로부터 질환별 건강 점수, 개인 건강 점수 및 질환별 미래 건강 점수를 추출할 수 있고, 이를 기반으로 개인별 건강 지표를 도출하여 사용자에게 제공할 수 있다.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.
비교분석부(360)는 건강점수 DB(335)로부터 개인 건강 점수를 추출할 수 있고, 이를 기반으로 사용자와 비슷한 환경 집단과 비교 분석하여 사용자에게 제공할 수 있다.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.
서비스평가부(370)는 개방형 개인 건강 기록 플랫폼(100)으로부터 의료기관 의료서비스 평가 정보를 도출할 수 있다. 의료기관 의료서비스 DB(375)는 의료기관 의료서비스 평가 정보를 저장할 수 있고, 사용자에게 제공할 수 있다.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.
도 4은 본 발명에 따른 질환별 건강 점수 산출 방법의 동작 순서도이다.4 is an operation flowchart of a method for calculating a health score for each disease according to the present invention.
도 4의 건강 점수 산출 방법은 도 3에 도시된 건강점수 산출부(330)에 의해 수행될 수 있으나, 동작 주체가 그에 한정되는 것은 아니다.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.
도 4에 도시된 바와 같이 본 발명에 따른 질환별 건강 점수를 산출하는 방법은, 개방형 개인 건강 기록 플랫폼으로부터 건강검진 데이터를 도출하고(S410), 진단 데이터도 도출한다(S420). 질환별 건강 점수를 산출하기 위한 점수 결정 요인을 산정하고(S430), 건강검진 데이터와 상기 진단 데이터를 기반으로 점수 결정 요인에 따라 질환별 개인 건강 점수를 산출한다(S440).As shown in FIG. 4, the method for calculating a health score for each disease according to the present invention 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).
구체적인 방법은 예를 들어, 고혈압에 대한 건강 점수를 산출하는 것으로 설명하겠다. 우선, 개방형 개인 건강 기록 플랫폼으로부터 고혈압에 관련된 사용자의 건강검진 데이터를 도출하고(S410), 고혈압에 관련된 진단 데이터를 도출한다(S420). 또한, 고혈압에 대한 건강 점수를 산출하기 위한 점수 결정 요인을 개방형 개인 건강 기록 플랫폼의 빅데이터를 참조하여 표 1과 같이 성별, 연령, 소득 분위, 고혈압 가족력, 암 가족력, BMI, 하루 흡연량 및 1회 음주량 등으로 산정한다(S430).The specific method will be described as calculating the health score for hypertension, for example. First, 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). In addition, 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).
Figure PCTKR2017014159-appb-T000001
Figure PCTKR2017014159-appb-T000001
점수 결정 요인이 산정되면, 고혈압과 관련된 사용자의 건강검진 데이터와 고혈압에 관련된 진단 데이터를 통하여 표 2와 같이 상기 점수 결정 요인에 따라 데이터를 매칭한다.When the score determining factor is calculated, 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.
Figure PCTKR2017014159-appb-T000002
Figure PCTKR2017014159-appb-T000002
Figure PCTKR2017014159-appb-M000001
Figure PCTKR2017014159-appb-M000001
점수 결정 요인에 따라 데이터를 매칭한 후, 수학식 1을 통해 질환별 건강 점수를 산출할 수 있다. 수학식 1에서 αi는 점수 결정 요인에 대한 가중치, Xi는 점수 결정 요인을 의미한다.After matching data according to the score determining factor, the health score for each disease may be calculated through Equation 1. In Equation 1, α i is a weight for the score determining factor, X i is a score determining factor.
Figure PCTKR2017014159-appb-M000002
Figure PCTKR2017014159-appb-M000002
수학식 1에 따라 예를 들어 설명한 고혈압의 경우를 대입하면, 수학식 2와 같이 고혈압에 대한 건강 점수가 산출된다(S440). 본 발명의 일 실시예에 따른 건강정보 예측 장치는 상술한 고혈압 외에도, 당뇨 및 비만 등 다양한 질환에 적용하여 점수를 산출할 수 있다.Substituting the case of hypertension described as an example according to Equation 1, a health score for hypertension is calculated as shown in Equation 2 (S440). The health information prediction apparatus according to an embodiment of the present invention may calculate a score by applying to various diseases such as diabetes and obesity, in addition to the above-described high blood pressure.
도 5는 본 발명의 일 실시예에 따른 건강관리부가 도출한 개인별 건강 지표의 출력 화면이다.5 is an output screen of the individual health indicator derived by the health care unit according to an embodiment of the present invention.
도 5와 같이 본 발명의 맞춤 건강 지표에는 질환별 건강 점수, 질환별 미래 건강 점수를 기반으로 향후 10년 예측 그래프, 맞춤 운동 및 그에 따른 운동 수행 시 미래의 예측 그래프가 제공될 수 있다.As shown in FIG. 5, 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.
또한, 본 발명의 맞춤 건강 지표가 제공하는 항목은 개인 건강 점수, 질환별 미래 건강 점수, 건강검진 데이터, 진단 데이터, 동일 집단 비교 분석결과 및 의료기관 의료서비스 평가 정보를 포함할 수 있다.In addition, 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.
다만, 본 발명의 맞춤 건강 지표가 제공하는 항목은 상술한 항목에 한정되는 것은 아니다.However, the items provided by the personalized health indicator of the present invention are not limited to the above items.
도 6은 본 발명에 따른 운동 정도 산정 방법의 동작 순서도이다.6 is an operation flowchart of the exercise degree calculation method according to the present invention.
앞서 살펴본 바와 같이 건강정보 예측 장치(300)는 사용자의 신체 정보 및 운동 정보를 기반으로 운동 정도를 판단하는 운동관리부(380)를 포함할 수 있다. 도 6에 도시된 운동 정도 산정 방법은 도 2에 도시된 운동관리부(380)에 의해 수행될 수 있으나, 동작 주체가 그에 한정되는 것은 아니다.As described above, 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.
운동관리부(380)의 운동 정도를 산정하는 방법은 개방형 개인 건강 기록 플랫폼(100) 또는 직접 서비스하는 개인 건강 기록 플랫폼으로부터 사용자의 신체정보 및 운동 정보를 수집하고(S610), 수학식 3과 같은 지정된 식에 따라 운동 점수를 산출한다(S620). 운동 점수가 100 이하인지 판단하여(S630), 100 이하이면 운동부족으로 산정하고(S640), 100 초과이면 과다운동으로 산정한다(S650).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).
Figure PCTKR2017014159-appb-M000003
Figure PCTKR2017014159-appb-M000003
Figure PCTKR2017014159-appb-M000004
Figure PCTKR2017014159-appb-M000004
수학식 3에서 Ep는 수학식 4에서 계산할 수 있다. 수학식 4에서 β1, β2, β3 및 β4는 각 사용자에 따라 다르게 측정되는 값이고, b는 몸무게, m은 근육 강도, q는 체지방 및 l은 신체적 활동 수준을 뜻한다. p는 개인 번호를, s는 현 상태를 및 r은 추천되는 상태를 의미한다. 수학식 4에서 q는 수학식 5로 계산할 수 있고, l은 수학식 6으로 계산할 수 있다.E p in Equation 3 may be calculated in Equation 4. In Equation 4, β 1 , β 2 , β 3, and β 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. In Equation 4, q may be calculated by Equation 5, and l may be calculated by Equation 6.
Figure PCTKR2017014159-appb-M000005
Figure PCTKR2017014159-appb-M000005
Figure PCTKR2017014159-appb-M000006
Figure PCTKR2017014159-appb-M000006
수학식 5에서 α1 및 α2는 각 사용자에 따라 다르게 측정되는 값이고, q1은 체지방 무게 그리고 q2는 근육 무게를 의미한다. 수학식 6에서 y1, y2 및 y3는 각 사용자에 따라 다르게 측정되는 값이고, l1은 작업량, l2는 폐활량 그리고 l3는 호흡 용량을 뜻한다.In Equation 5, α 1 and α 2 are values measured differently according to each user, q 1 means body fat weight and q 2 means muscle weight. In 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.
운동관리부(380)는 상술한 바와 같이, 운동 점수 및 운동 정도를 산정함으로써, 사용자의 운동량을 추적 및 관리할 수 있고, 운동 점수 및 운동 정도를 사용자에게 제공할 수 있다. 또한, 운동관리부(380)은 사용자가 외부기관(500)으로부터 맞춤 운동 서비스 및 체력 관리 서비스를 제공받기 위해 외부기관(500)에게 운동 점수 및 운동 정도를 제공할 수 있다.As described above, 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. In addition, 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.
운동 점수를 산출하는 식은 개인별 운동 실천 지표(Personal Exercise Activity Index, PEAI)라고 지칭할 수 있으며, 수학식 3에 한정되는 것은 아니다.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.
도 7은 본 발명에 따른 절대 스트레스 정도 산정 방법의 동작 순서도이다.7 is an operation flowchart of a method for calculating an absolute stress level according to the present invention.
건강정보 예측 장치(300)는 사용자의 스트레스 정보를 기반으로 절대 스트레스 정도를 판단하는 절대 스트레스 관리부(385)를 포함할 수 있다. 도 7에 도시된 절대 스트레스 정도 산정 방법은 도 2에 도시된 절대 스트레스 관리부(385)에 의해 수행될 수 있으나, 동작 주체가 그에 한정되는 것은 아니다.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.
절대 스트레스 관리부(385)의 스트레스 정도를 산정하는 방법은 개방형 개인 건강 기록 플랫폼(100) 또는 직접 서비스하는 개인 건강 기록 플랫폼으로부터 사용자의 스트레스 정보를 수집하고(S710), 수학식 7과 같은 지정된 식에 따라 절대 스트레스 점수를 산출한다(S720). 절대 스트레스 점수가 100 이하인지 판단하여(S730), 100 이하이면 스트레스 안정으로 산정하고(S740), 100 초과이면 스트레스 과다로 산정한다(S750).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).
Figure PCTKR2017014159-appb-M000007
Figure PCTKR2017014159-appb-M000007
Figure PCTKR2017014159-appb-M000008
Figure PCTKR2017014159-appb-M000008
수학식 7에서 ASp는 수학식 8을 통해 계산할 수 있다. 수학식 8에서 βp는 각 사용자에 따라 다르게 측정되는 값이고, hrn는 n번째 데이터에서의 심박수, hrs는 안정적인 상황에서의 심박수 그리고 p는 개인 번호를 의미한다.AS p in Equation 7 may be calculated by Equation 8. In Equation 8, β 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, and p is an individual number.
절대 스트레스 관리부(385)는 상술한 바와 같이, 절대 스트레스 점수 및 스트레스 정도를 산정함으로써, 사용자의 직무별·부서별 근무 환경 관리 및 정신 건강 관리를 할 수 있고, 절대 스트레스 점수 및 스트레스 정도를 사용자에게 제공할 수 있다. 또한, 절대 스트레스 관리부(385)는 사용자가 외부기관(500)으로부터 멘탈케어 서비스 등을 제공받기 위해 외부기관(500)에게 절대 스트레스 점수 및 스트레스 정도를 제공할 수 있다.Absolute stress management unit 385, as described above, 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. In addition, 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.
절대 스트레스 점수를 산출하는 식은 개인별 절대 스트레스 지표(Personal Absolute Stress Index, PASI)라고 지칭할 수 있으며, 수학식 7에 한정되는 것은 아니다.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.
도 8은 본 발명에 따른 상대 스트레스 정도 산정 방법의 동작 순서도이다.8 is a flowchart illustrating a method of calculating a relative stress level according to the present invention.
건강정보 예측 장치(300)는 절대 스트레스 점수를 기반으로 상대 스트레스 정도를 판단하는 상대 스트레스 관리부(390)를 포함할 수 있다. 도 8에 도시된 상대 스트레스 정도 산정 방법은 도 2에 도시된 상대 스트레스 관리부(390)에 의해 수행될 수 있으나, 동작 주체가 그에 한정되는 것은 아니다.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.
상대 스트레스 관리부(390)의 스트레스 정도를 산정하는 방법은 절대 스트레스 관리부(385)로부터 사용자의 절대 스트레스 점수를 수집하고(S810), 수학식 9와 같은 지정된 식에 따라 상대 스트레스 점수를 산출한다(S820). 상대 스트레스 점수가 100 이하인지 판단하여(S830), 100 이하이면 직장내 스트레스가 평균 이하인 것으로 산정하고(S840), 100 초과이면 직장내 스트레스가 평균 초과인 것으로 산정한다(S850).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).
Figure PCTKR2017014159-appb-M000009
Figure PCTKR2017014159-appb-M000009
Figure PCTKR2017014159-appb-M000010
Figure PCTKR2017014159-appb-M000010
수학식 9에서 RSp는 수학식 10을 통해 계산할 수 있다. 수학식 10에서 AS는 절대 스트레스 점수, p는 개인 번호 그리고 n은 회사의 전체 인원수을 의미한다.RS p in Equation 9 may be calculated through Equation 10. In Equation 10, AS is an absolute stress score, p is an individual number, and n is the total number of people in the company.
상대 스트레스 관리부(390)는 상술한 바와 같이, 상대 스트레스 점수 및 스트레스 정도를 산정함으로써, 사용자의 직무별·부서별 근무 환경 관리 및 정신 건강 관리를 할 수 있고, 상대 스트레스 점수 및 스트레스 정도를 사용자에게 제공할 수 있다. 또한, 상대 스트레스 관리부(390)는 사용자가 외부기관(500)으로부터 멘탈케어 서비스 등을 제공받기 위해 외부기관(500)에게 상대 스트레스 점수 및 스트레스 정도를 제공할 수 있다.Relative stress management unit 390, as described above, 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. In addition, 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.
상대 스트레스 점수를 산출하는 식은 개인별 상대 스트레스 지표(Personal Relative Stress Index, PRSI)라고 지칭할 수 있으며, 수학식 9에 한정되는 것은 아니다.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.
본 발명의 실시예에 따른 방법의 동작은 컴퓨터로 읽을 수 있는 기록매체에 컴퓨터가 읽을 수 있는 프로그램 또는 코드로서 구현하는 것이 가능하다. 컴퓨터가 읽을 수 있는 기록매체는 컴퓨터 시스템에 의해 읽혀질 수 있는 데이터가 저장되는 모든 종류의 기록장치를 포함한다. 또한 컴퓨터가 읽을 수 있는 기록매체는 네트워크로 연결된 컴퓨터 시스템에 분산되어 분산 방식으로 컴퓨터로 읽을 수 있는 프로그램 또는 코드가 저장되고 실행될 수 있다.The operation of the method according to an embodiment of the present invention can be embodied as a computer readable program or code on a computer readable recording medium. 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.
또한, 컴퓨터가 읽을 수 있는 기록매체는 롬(rom), 램(ram), 플래시 메모리(flash memory) 등과 같이 프로그램 명령을 저장하고 수행하도록 특별히 구성된 하드웨어 장치를 포함할 수 있다. 프로그램 명령은 컴파일러(compiler)에 의해 만들어지는 것과 같은 기계어 코드뿐만 아니라 인터프리터(interpreter) 등을 사용해서 컴퓨터에 의해 실행될 수 있는 고급 언어 코드를 포함할 수 있다.In addition, 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.
본 발명의 일부 측면들은 장치의 문맥에서 설명되었으나, 그것은 상응하는 방법에 따른 설명 또한 나타낼 수 있고, 여기서 블록 또는 장치는 방법 단계 또는 방법 단계의 특징에 상응한다. 유사하게, 방법의 문맥에서 설명된 측면들은 또한 상응하는 블록 또는 아이템 또는 상응하는 장치의 특징으로 나타낼 수 있다. 방법 단계들의 몇몇 또는 전부는 예를 들어, 마이크로프로세서, 프로그램 가능한 컴퓨터 또는 전자 회로와 같은 하드웨어 장치에 의해(또는 이용하여) 수행될 수 있다. 몇몇의 실시예에서, 가장 중요한 방법 단계들의 하나 이상은 이와 같은 장치에 의해 수행될 수 있다.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.
실시예들에서, 프로그램 가능한 로직 장치(예를 들어, 필드 프로그머블 게이트 어레이)가 여기서 설명된 방법들의 기능의 일부 또는 전부를 수행하기 위해 사용될 수 있다. 실시예들에서, 필드 프로그머블 게이트 어레이는 여기서 설명된 방법들 중 하나를 수행하기 위한 마이크로프로세서와 함께 작동할 수 있다. 일반적으로, 방법들은 어떤 하드웨어 장치에 의해 수행되는 것이 바람직하다.In embodiments, a programmable logic device (eg, a field programmable gate array) may be used to perform some or all of the functionality of the methods described herein. In embodiments, the field programmable gate array may operate in conjunction with a microprocessor to perform one of the methods described herein. In general, the methods are preferably performed by any hardware apparatus.
이상 본 발명의 바람직한 실시예를 참조하여 설명하였지만, 해당 기술 분야의 숙련된 당업자는 하기의 특허 청구의 범위에 기재된 본 발명의 사상 및 영역으로부터 벗어나지 않는 범위 내에서 본 발명을 다양하게 수정 및 변경시킬 수 있음을 이해할 수 있을 것이다.Although it has been described above with reference to the preferred embodiment of the present invention, those skilled in the art will be able to variously modify and change the present invention without departing from the spirit and scope of the invention described in the claims below. It will be appreciated.

Claims (21)

  1. 개방형 개인 건강 기록(Personal Health Record, PHR) 플랫폼을 이용하여 건강정보를 예측하는 장치로서,A device for predicting health information using an open personal health record (PHR) platform,
    상기 개방형 개인 건강 기록 플랫폼으로부터 건강검진 데이터를 도출하는 건강검진부;A health examination unit for deriving health examination data from the open personal health record platform;
    상기 개방형 개인 건강 기록 플랫폼으로부터 진단 데이터를 도출하는 진단부; 및A diagnosis unit for deriving diagnosis data from the open personal health record platform; And
    상기 개방형 개인 건강 기록 플랫폼으로부터 질환별 점수 결정 요인을 산정하고, 상기 건강검진 데이터 및 상기 진단 데이터를 기반으로 상기 질환별 점수 결정 요인에 따라 질환별 건강 점수를 산출하고, 상기 질환별 건강 점수에 따라 개인 건강 점수를 산출하는 건강점수 산출부를 포함하는, 건강정보 예측 장치.A disease-determining factor for each disease is calculated from the open personal health record platform, and a health score for each disease is calculated according to the disease-specific score determining factor based on the health examination data and the diagnosis data, and according to the health score for each disease. Health information prediction device comprising a health score calculation unit for calculating a personal health score.
  2. 청구항 1에 있어서,The method according to claim 1,
    상기 건강검진 데이터 및 상기 진단 데이터를 기반으로 상기 질환별 점수 결정 요인에 따라 질환별 미래 건강 점수를 산출하는 미래건강점수 산출부를 더 포함하는, 건강정보 예측 장치The health information predicting apparatus further includes a future health score calculation unit configured to calculate a future health score for each disease based on the score determining factors for each disease based on the health examination data and the diagnosis data.
  3. 청구항 2에 있어서,The method according to claim 2,
    상기 개인 건강 점수와 상기 질환별 미래 건강 점수를 기반으로 개인별 건강 지표를 도출하는 건강관리부를 더 포함하는, 건강정보 예측 장치.The health information predicting apparatus further comprises a health management unit for deriving an individual health index based on the personal health score and the future health score for each disease.
  4. 청구항 1에 있어서,The method according to claim 1,
    상기 질환별 건강 점수를 기반으로 사용자와 비슷한 환경 집단을 비교 분석하는 비교분석부를 더 포함하는, 건강정보 예측 장치.The health information prediction device further comprises a comparison and analysis unit for comparing and analyzing the environment group similar to the user based on the health score for each disease.
  5. 청구항 1에 있어서,The method according to claim 1,
    상기 개방형 개인 건강 기록 플랫폼으로부터 의료기관 의료서비스 평가 정보를 도출하는 서비스평가부를 더 포함하는, 건강정보 예측 장치.And a service evaluation unit for deriving medical institution medical service evaluation information from the open personal health record platform.
  6. 청구항 1에 있어서,The method according to claim 1,
    건강 및 질병관리 지식을 수집하는 정보수집부;An information collector for collecting health and disease management knowledge;
    지식 저장소를 참조하여 피드백하는 피드백부;A feedback unit for feeding back with reference to the knowledge repository;
    개인생활건강정보기록 인터페이스;Personal health information record interface;
    서비스 시스템 통합 및 연계용 어플리케이션 프로그래밍 인터페이스(Application Programming Interface, API);Application Programming Interface (API) for Service System Integration and Integration;
    정형데이터와 비정형데이터를 연계하는 데이터연계부;A data linker linking the structured data with the unstructured data;
    빅데이터를 활용한 클라우드 컴퓨팅 서비스 제공하는 클라우드서비스부; 및A cloud service unit providing cloud computing services using big data; And
    개인 건강 기록의 공공데이터화를 통한 개인별 건강관리를 예측하는 건강예측부를 포함하는 개인 건강 기록 플랫폼을 더 포함하는, 건강정보 예측 장치.The health information prediction device further comprises a personal health record platform including a health prediction unit for predicting personal health management through public data of the personal health record.
  7. 청구항 1에 있어서,The method according to claim 1,
    건강정보 관련 데이터를 활용한 실시간 유헬스케어를 제공하는 유헬스케어부;U-healthcare unit that provides real-time u-healthcare using health information-related data;
    건강보험 빅데이터를 활용한 보호되는 건강 정보(Protected Health Information, PHI)의 모니터링을 제공하는 PHI모니터부;A PHI monitor unit that provides monitoring of protected health information (PHI) using health insurance big data;
    질병 데이터 분석을 통한 질병 예방 프로그램을 제공하는 질병예방부;Disease Prevention Department for providing disease prevention program through disease data analysis;
    유헬스케어를 활용한 진단, 치료, 사후관리의 보건의료서비스를 제공하는 의료서비스부;Medical Services Department, which provides medical services for diagnosis, treatment, and follow-up using u-healthcare;
    예방접종 데이터 연계를 연계시켜 예방접종 시기 및 기존 개인 건강 기록 자료를 통해 추천서비스를 제공하는 통합적인 데이터 관리를 수행하는 데이터관리부;A data management unit for integrating vaccination data link to perform integrated data management to provide recommendation services through vaccination timing and existing personal health records;
    건강검진 결과, 진료 및 투약내역 분석을 통한 건강예측 가능 모형을 제공하는 예측모형부; 및Prediction model unit for providing a health predictable model through the medical examination results, treatment and medication history analysis; And
    개인 유전자정보와 건강형태 정보 통합을 통한 질병발생확률을 분석하는 질병분석부를 더 포함하는, 건강정보 예측 장치.The health information prediction apparatus further comprises a disease analysis unit for analyzing the probability of disease occurrence through integration of individual genetic information and health type information.
  8. 청구항 1에 있어서,The method according to claim 1,
    사용자의 신체 정보 및 운동 정보를 수집하고, 상기 신체 정보 및 운동 정보를 기반으로 운동 점수를 산출하고, 상기 운동 점수에 따라 사용자의 운동 정도를 산정하는 운동관리부를 더 포함하는, 건강정보 예측 장치.The exercise information predicting apparatus further comprises: an exercise management unit collecting body information and exercise information of the user, calculating an exercise score based on the body information and exercise information, and calculating a degree of exercise of the user according to the exercise score.
  9. 청구항 1에 있어서,The method according to claim 1,
    사용자의 스트레스 정보를 수집하고, 상기 스트레스 정보를 기반으로 절대 스트레스 점수를 산출하고, 상기 절대 스트레스 점수에 따라 사용자의 스트레스 정도를 산정하는 절대 스트레스 관리부를 더 포함하는, 건강정보 예측 장치.And an absolute stress management unit for collecting stress information of the user, calculating an absolute stress score based on the stress information, and calculating a degree of stress of the user according to the absolute stress score.
  10. 청구항 9에 있어서,The method according to claim 9,
    상기 절대 스트레스 점수를 기반으로 상대 스트레스 점수를 산출하고, 상기 상대 스트레스 점수에 따라 사용자의 스트레스 정도를 산정하는 상대 스트레스 관리부를 더 포함하는, 건강정보 예측 장치.Comprising a relative stress score based on the absolute stress score, and further comprising a relative stress management unit for calculating the degree of stress of the user according to the relative stress score, health information prediction device.
  11. 개방형 개인 건강 기록(Personal Health Record, PHR) 플랫폼을 이용하여 건강정보를 예측하는 방법으로서,As a method of predicting health information using an open personal health record (PHR) platform,
    상기 개방형 개인 건강 기록 플랫폼으로부터 건강검진 데이터, 진단 데이터및 질환별 점수 결정 요인을 도출하는 단계; 및Deriving health check data, diagnosis data, and disease-specific score determining factors from the open personal health record platform; And
    상기 건강검진 데이터 및 진단 데이터를 기반으로 상기 질환별 점수 결정 요인에 따라 질환별 건강 점수를 산출하는 단계를 포함하는, 건강정보 예측 방법.And calculating a health score for each disease according to the disease score determining factor based on the health examination data and the diagnosis data.
  12. 청구항 11에 있어서,The method according to claim 11,
    상기 질환별 건강 점수에 따라 개인 건강 점수를 산출하는 단계를 더 포함하는, 건강정보 예측 방법.Comprising the step of calculating the individual health score according to the disease-specific health score, health information prediction method.
  13. 청구항 11에 있어서,The method according to claim 11,
    상기 건강검진 데이터 및 진단 데이터를 기반으로 상기 질환별 점수 결정 요인에 따라 질환별 미래 건강 점수를 산출하는 단계를 더 포함하는, 건강정보 예측 방법.And calculating a future health score for each disease according to the disease-determining score determining factor based on the health examination data and the diagnosis data.
  14. 청구항 13에 있어서,The method according to claim 13,
    상기 질환별 건강 점수와 상기 질환별 미래 건강 점수를 기반으로 개인별 건강 지표를 도출하는 단계를 더 포함하는, 건강정보 예측 방법.Deriving a health indicator for each individual based on the health score for each disease and the future health score for each disease, health information prediction method.
  15. 청구항 11에 있어서,The method according to claim 11,
    상기 질환별 건강 점수를 기반으로 사용자와 비슷한 환경 집단을 비교 분석하는 단계를 더 포함하는, 건강정보 예측 방법.Comprising the step of comparing and analyzing similar environmental groups with the user based on the disease-specific health score, health information prediction method.
  16. 청구항 11에 있어서,The method according to claim 11,
    상기 개방형 개인 건강 기록 플랫폼으로부터 의료기관 의료서비스 평가 데이터를 도출하는 단계를 더 포함하는, 건강정보 예측 방법.Deriving a medical institution medical service evaluation data from the open personal health record platform, health information prediction method.
  17. 청구항 11에 있어서,The method according to claim 11,
    개인 건강 기록 플랫폼을 구축하는 단계를 더 포함하고,Further comprising building a personal health record platform,
    상기 개인 건강 기록 플랫폼을 구축하는 단계는,Building the personal health record platform,
    건강 및 질병 지식을 수집하는 단계;Collecting health and disease knowledge;
    지식저장소를 참조하여 피드백하는 단계;Feedback with reference to the knowledge repository;
    개인생활건강정보기록 인터페이스를 이용하는 단계;Using a personal health information record interface;
    서비스 시스템 통합 및 연계용 어플리케이션 프로그래밍 인터페이스(Application Programming Interface, API)를 이용하는 단계;Using an application programming interface (API) for service system integration and linkage;
    정형데이터와 비정형데이터를 연계하는 단계;Linking the structured data with the unstructured data;
    빅데이터를 활용한 클라우드 컴퓨팅 서비스를 제공하는 단계; 및Providing a cloud computing service utilizing big data; And
    개인 건강 기록의 공공데이터화를 통한 개인별 건강관리를 예측하는 단계를 포함하는, 건강정보 예측 방법.Predicting individual health care through the public data of the personal health record, health information prediction method.
  18. 청구항 11에 있어서,The method according to claim 11,
    건강정보 관련 데이터를 활용한 실시간 유헬스케어를 제공하는 단계;Providing a real-time u-healthcare utilizing health information-related data;
    건강보험 빅데이터를 활용한 보호되는 건강 정보(Protected Health Information, PHI)를 모니터링하는 단계;Monitoring protected health information (PHI) using health insurance big data;
    질병 데이터 분석을 통한 질병 예방 프로그램를 제공하는 단계;Providing a disease prevention program through disease data analysis;
    유헬스케어를 활용한 진단, 치료, 사후관리의 보건의료서비스를 제공하는 단계;Providing health care services for diagnosis, treatment, and follow-up using u-healthcare;
    예방접종 데이터를 연계시켜 예방접종 시기 및 기존 개인 건강 기록 자료를 통해 추천서비스를 제공하는 통합적인 데이터 관리를 제공하는 단계;Linking vaccination data to provide integrated data management to provide referral services through vaccination timing and existing personal health records;
    건강검진 결과, 진료 및 투약내역 분석을 통한 건강예측 가능 모형을 제공하는 단계; 및Providing a health predictable model through health examination results, medical treatment and medication history analysis; And
    개인 유전자정보와 건강형태 정보 통합을 통한 질병발생확률 분석하는 단계를 더 포함하는, 건강정보 예측 방법.Further comprising the step of analyzing the probability of disease occurrence through integration of individual genetic information and health type information, health information prediction method.
  19. 청구항 11에 있어서,The method according to claim 11,
    사용자의 신체 정보 및 운동 정보를 수집하는 단계;Collecting body information and exercise information of the user;
    상기 신체 정보 및 운동 정보를 기반으로 운동 점수를 산출하는 단계; 및Calculating an exercise score based on the body information and the exercise information; And
    상기 운동 점수에 따라 사용자의 운동 정도를 산정하는 단계를 더 포함하는, 건강정보 예측 방법.Comprising the step of calculating the exercise degree of the user according to the exercise score, Health information prediction method.
  20. 청구항 11에 있어서,The method according to claim 11,
    사용자의 스트레스 정보를 수집하는 단계;Collecting stress information of the user;
    상기 스트레스 정보를 기반으로 절대 스트레스 점수를 산출하는 단계; 및Calculating an absolute stress score based on the stress information; And
    상기 절대 스트레스 점수에 따라 사용자의 스트레스 정도를 산정하는 단계를 더 포함하는, 건강정보 예측 방법.Comprising the step of calculating the degree of stress of the user according to the absolute stress score, Health information prediction method.
  21. 청구항 20에 있어서,The method of claim 20,
    상기 절대 스트레스 점수를 기반으로 상대 스트레스 점수를 산출하는 단계; 및Calculating a relative stress score based on the absolute stress score; And
    상기 상대 스트레스 점수에 따라 사용자의 스트레스 정도를 산정하는 단계를 더 포함하는, 건강정보 예측 방법.Comprising the step of calculating the stress level of the user according to the relative stress score, Health information prediction method.
PCT/KR2017/014159 2016-12-06 2017-12-05 Device and method for health information prediction using big data WO2018105995A2 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US16/465,543 US20200005944A1 (en) 2016-12-06 2017-12-05 Device and method for health information prediction using big data

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR20160164949 2016-12-06
KR10-2016-0164949 2016-12-06
KR1020170016988A KR101970947B1 (en) 2016-12-06 2017-02-07 Apparatus and method for predicting health information using big data
KR10-2017-0016988 2017-02-07

Publications (2)

Publication Number Publication Date
WO2018105995A2 true WO2018105995A2 (en) 2018-06-14
WO2018105995A3 WO2018105995A3 (en) 2018-08-09

Family

ID=62492082

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2017/014159 WO2018105995A2 (en) 2016-12-06 2017-12-05 Device and method for health information prediction using big data

Country Status (1)

Country Link
WO (1) WO2018105995A2 (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109659001A (en) * 2018-12-18 2019-04-19 延安大学 A kind of anti-cancer supervisory systems and method
CN109887566A (en) * 2019-02-26 2019-06-14 卫宁健康科技集团股份有限公司 The intelligent management and system of Electronic Health Record
CN110033866A (en) * 2019-03-08 2019-07-19 平安科技（深圳）有限公司 Healthalert method, apparatus, computer equipment and storage medium
CN110689257A (en) * 2019-09-24 2020-01-14 北京市天元网络技术股份有限公司 Fast-moving-away product business supervision method and device based on operator big data
CN110797121A (en) * 2019-10-29 2020-02-14 浪潮天元通信信息系统有限公司 Remote intelligent health analysis system and method based on Internet of things

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20150112423A (en) * 2014-03-28 2015-10-07 한양대학교 산학협력단 Virutal hospital system, method for generating virutal hospital and apparatus for performing the method, method for providing medical service
KR101510600B1 (en) * 2014-11-11 2015-04-08 국민건강보험공단 Bigdata health record system
KR101961165B1 (en) * 2014-11-25 2019-07-17 한국전자통신연구원 Open health management apparatus and method
KR101577068B1 (en) * 2015-07-27 2015-12-11 주식회사 그린메디넷 Smart Healthy Check kiosk system and method for using thereof
KR20160055749A (en) * 2016-04-29 2016-05-18 아주대학교산학협력단 System and method for service design lifestyle

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109659001A (en) * 2018-12-18 2019-04-19 延安大学 A kind of anti-cancer supervisory systems and method
CN109659001B (en) * 2018-12-18 2023-08-04 延安大学 Cancer prevention supervision system and method
CN109887566A (en) * 2019-02-26 2019-06-14 卫宁健康科技集团股份有限公司 The intelligent management and system of Electronic Health Record
CN110033866A (en) * 2019-03-08 2019-07-19 平安科技（深圳）有限公司 Healthalert method, apparatus, computer equipment and storage medium
CN110033866B (en) * 2019-03-08 2023-08-11 平安科技（深圳）有限公司 Health reminding method, device, computer equipment and storage medium
CN110689257A (en) * 2019-09-24 2020-01-14 北京市天元网络技术股份有限公司 Fast-moving-away product business supervision method and device based on operator big data
CN110689257B (en) * 2019-09-24 2022-09-09 北京市天元网络技术股份有限公司 Operator big data-based fast-moving consumer goods business supervision method and device
CN110797121A (en) * 2019-10-29 2020-02-14 浪潮天元通信信息系统有限公司 Remote intelligent health analysis system and method based on Internet of things

Also Published As

Publication number Publication date
WO2018105995A3 (en) 2018-08-09

Similar Documents

Publication Publication Date Title
WO2018105995A2 (en) Device and method for health information prediction using big data
KR101970947B1 (en) Apparatus and method for predicting health information using big data
Haydar et al. Comparison of QSOFA score and SIRS criteria as screening mechanisms for emergency department sepsis
WO2019168334A1 (en) Apparatus for data processingfor predicting dementia through machine learning, method thereof, and recording medium storing same
CN109524069B (en) Medical data processing method and device, electronic equipment and storage medium
WO2021159761A1 (en) Pathological data analysis method and apparatus, and computer device and storage medium
WO2022145782A2 (en) Big data and cloud system-based artificial intelligence emergency medical care decision making and emergency patient transporting system and method therefor
WO2022169037A1 (en) Method for predicting mental health and providing mental health solutions by learning psychological indicator data and physical indicator data on basis of machine learning, and mental health evaluation device using same
WO2016068391A1 (en) Method for analyzing individual characteristics of patient and apparatus therefor
TWI521467B (en) Nursing decision support system
WO2020119403A1 (en) Hospitalization data abnormity detection method, apparatus and device, and readable storage medium
WO2022211385A1 (en) Health care consultation system using distribution of disease prediction values
WO2015199296A1 (en) Emergency psychiatric mental state prediction model-based remote emergency psychiatry system and method
WO2019225910A1 (en) Method and system for predicting and analyzing stroke severity using nihss
WO2021215809A1 (en) System and method for providing early diagnosis of cognitive disorder and community care matching service for elderly
WO2020119131A1 (en) Medication scheme abnormality identification method and device, terminal, and readable storage medium
WO2020119118A1 (en) Abnormal data processing method, apparatus and device, and computer readable storage medium
CN114334123A (en) Cognition assessment system suitable for mild cognitive impairment rapid detection
Pagali et al. Delirium occurrence and association with outcomes in hospitalized COVID-19 patients
Chyten-Brennan et al. Algorithm to identify transgender and gender nonbinary individuals among people living with HIV performs differently by age and ethnicity
CN107066816B (en) Medical treatment guidance method and device based on clinical data and server
WO2016200243A1 (en) Computing apparatus and method for aiding classification of mibyeong
JP2014199597A (en) Medical examination support program, medical examination support method and medical examination support device
Tay Hospital readmission in stroke survivors one year versus three years after discharge from inpatient rehabilitation: prevalence and associations in an asian cohort
JP2005196510A (en) Medical institution search system

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17879622

Country of ref document: EP

Kind code of ref document: A2

NENP Non-entry into the national phase in:

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17879622

Country of ref document: EP

Kind code of ref document: A2