| Project/Area Number |
17K19793
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Society medicine, Nursing, and related fields
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| Research Institution | Kyoto University (2020)
The University of Tokyo (2017-2019) |
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Principal Investigator |
KONDO NAOKI 京都大学, 医学研究科, 教授 (20345705)
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| Project Period (FY) |
2017-06-30 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2019: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2018: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2017: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | 健康格差 / 機械学習 / マーケティング / 社会疫学 / 生活保護 / 健康の社会的決定要因 / 行動科学 / 生活保護制度 / セグメント分析 / 人工知能 / 情報科学 |
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| Outline of Final Research Achievements |
Economic deprivation makes health care difficult. Therefore, we have built a support system that applies marketing techniques for them. Using a database of welfare recipients, we developed a population segmentation algorithm for providing appropriate care to welfare recipients based on information such as the background that led to welfare, adult and life history, and health literacy. We obtained welfare management data and medical insurance receipts to segment the recipients and implemented them in the system. The relationships between the risk of chronic diseases for children, adults, and older adults and the social status (isolation, non-working, etc.) of welfare recipients was evaluated. We developed and published a tool for screening needy and isolated people with easy-to-ask questions.
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| Academic Significance and Societal Importance of the Research Achievements |
R3年度から開始された生活保護の被保護者健康管理支援事業等、社会的な課題を踏まえた健康管理支援を広く普及するための実際のシステム構築を達成した。本研究は複数の科研プロジェクトに引き継がれ、さらなるシステムのアップデートと、そのデータを用いた実証分析へと拡張している。機械学習アルゴリズムを生活保護制度に関するビッグデータに活用して福祉を推進するための学術と社会の橋渡し研究として社会的意義は大きいと考える。
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