Construction of The Health Process Model System based on State Transition Probability to utilize NDB Big Data
| Project/Area Number |
17K01820
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Applied health science
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| Research Institution | Nagoya City University |
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Principal Investigator |
Miyauchi Yoshiaki 名古屋市立大学, 大学院看護学研究科, 准教授 (70410511)
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| Co-Investigator(Kenkyū-buntansha) |
西村 治彦 兵庫県立大学, 応用情報科学研究科, 教授 (40218201)
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | ビッグデータ / 状態遷移確率 / ビックデータ / 健診情報 |
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| Outline of Final Research Achievements |
We reexamined the model structure and modified the program so that the health process model based on the state transition probability that we constructed earlier corresponds to the data structure of NDB. For the expression of the health status of the examinees, we used the binarization of the health examination data based on the health examination judgment standard value and the expression of the health condition of 16 states by logical sum. Next, in order to improve the accuracy and reliability of the health process model as the data is accumulated year by year, we have developed a mechanism to automatically calculate and update by applying AI technology. In addition, we worked on Android application development so that the examinees can utilize the health process model on a daily basis. By integrating them, the basic configuration of the "health process model system", which is the purpose, was realized.
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| Academic Significance and Societal Importance of the Research Achievements |
特定健診をはじめとするデータヘルス計画における保健事業の成果として個人単位での健診等のデータが大規模に年々蓄積されていくNDBビックデータに親和性の高い保健指導サポートシステムを構築したことにより、NDBビックデータに基づいた高精度な健康セルフマネジメントを受診者自らが行うことができるようになると考えている。そして、これはデータヘルス計画推進への貢献のみならず、2035年の保健医療へ向けたイノベーションと情報基盤の整備と活用への貢献へつながるものと考えている。
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Report
(5 results)
Research Products
(11 results)
