Reaserch in modeling of the health state transition with physical checkup data
Project/Area Number |
17K00423
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Life / Health / Medical informatics
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Research Institution | University of Occupational and Environmental Health, Japan |
Principal Investigator |
Asakawa Takeshi 産業医科大学, 情報管理センター, 准教授 (90419515)
|
Project Period (FY) |
2017-04-01 – 2020-03-31
|
Project Status |
Completed (Fiscal Year 2019)
|
Budget Amount *help |
¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000)
Fiscal Year 2017: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
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Keywords | 医療ビッグデータ / 健診データ / レセプトデータ / 医療費 / 医療費推計 / 状態空間モデル / 健康状態遷移 / 予測モデル / データヘルス |
Outline of Final Research Achievements |
In this study, using the medical big data such as medical examinations which are widely conducted and receipt records generated when visiting hospitals, we developed a method to estimate the averaged medical cost with which a person with a certain disease can maintain their health condition. It is a highly versatile method that can be used to estimate how much medical costs will increase if the person who already has the disease has another disease. Our method was already registered as a patent in Japan because of its novelty and feasibility.
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Academic Significance and Societal Importance of the Research Achievements |
従来、病気の状態や合併症などの非常に複雑な個々の患者背景の相違から、既に蓄積された膨大な医療データにも関わらず、ある疾病の治療に平均幾らの医療費が費やされているのかという問いに十分に答えられていなかった。これが健康状態の遷移を考える上で最も早急に解決されるべき課題だと感じ、本研究では統計科学・機械学習を援用して傷病別の医療費推定方法の開発を行った。この手法を用いると、ある基本疾患を持った人が、合併症併発時の医療費の上昇を試算することも可能となり、健康状態の悪化を医療費で数値化して現実的に把握出来るようになる。これは今後の社会のデータヘルス事業の礎となるべき手法である。
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Report
(4 results)
Research Products
(4 results)