| Project/Area Number |
22K10434
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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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| Review Section |
Basic Section 58010:Medical management and medical sociology-related
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| Research Institution | Hokkaido University |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Project Status |
Completed (Fiscal Year 2024)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2024: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2023: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2022: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
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| Keywords | レセプトデータ / 特定健診データ / リアルワールドデータ / AI / GIS / 医療資源配置 / 生活習慣病 / 潜在クラス分析 / 機械学習 / 発症予測モデル / 人工知能 / 地図情報システム / 地理的アクセス性 / レセプト / Ⅱ型糖尿病 / ベイジアンネットワーク / ランダムフォレスト / XG Boost |
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| Outline of Research at the Start |
本研究では北海道の複数の市町村や健康保険組合から提供された特定健康診査データ、医療保険請求データ、介護保険請求データ、被保険者管理台帳を用いて、市町村毎の保健福祉事業政策策定に向け、継続可能な地域医療政策の立案を目標に、以下の研究を行う。
(1) 特定健診後の受診勧奨と医療費の傾向
(2) 疾患別受診傾向と介護費の関係
(3) 地図情報システム(GIS)による受診行動の可視化
(4）疾患・合併症の医療費の人工知能(AI)予測モデルの構築
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| Outline of Final Research Achievements |
This study found no significant difference in medical costs between those who received lifestyle disease checkup reminders and those who did not. However, individuals who visited medical institutions after health checkups had higher medical costs. Dialysis and facility use were strongly associated with worsening care needs, and visits for various diseases contributed to increased long-term care costs. GIS-based analysis of health checkup behaviors showed positive correlations between the number of examinees and test items, with seasonal and gender-related trends also observed. Furthermore, disease prediction models using AI and statistical methods identified BMI, waist circumference, and alcohol consumption as major risk factors for the onset of diabetes and hyperuricaemia, indicating their usefulness in identifying targets for lifestyle interventions and enabling personalized risk prediction.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、医療ビッグデータとAIを活用し、生活習慣病や高尿酸血症の発症予測モデルを構築し、予防的介入のためのリスク因子を特定することで、医療費の削減や重症化予防に貢献する。また、受診勧奨と医療・介護費の関係を分析し、効果的な保健指導政策の検討材料を提供した。さらに、GISを用いた受診行動分析や地域間の医療アクセスの可視化により、地域格差の是正や医療資源の最適配置に向けた科学的根拠を示し、政策立案に資する意義を持つ。
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