Validation and proposal of a framework for the application of artificial intelligence techniques to epidemiological data

2021 Fiscal Year Final Research Report

• Project/Area Number: 19K19433
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 58030:Hygiene and public health-related: excluding laboratory approach
• Research Institution: University of Yamanashi
• Principal Investigator: Ooka Tadao 山梨大学, 大学院総合研究部, 助教 (40803987)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: 疾患予測モデル / 糖尿病 / 健康診断 / 機械学習 / ランダムフォレスト

Outline of Final Research Achievements

By utilizing data from about 20 years of health checkups at health checkup facilities, we have succeeded in developing an artificial intelligence model that can accurately predict who will have a sharp rise in HbA1c, an important indicator of type 2 diabetes, based on the results of the previous year's health checkups. By validating these models, we identified factors (e.g., cholesterol levels, blood pressure) that are important in predicting the onset of type 2 diabetes.
Furthermore, by developing this model, we have also developed an artificial intelligence model that can accurately predict the results of health checkups one and three years from now, based on the results of past health checkups.
In the future, a randomized controlled trial will be conducted to confirm whether the model can be used in actual health checkups and health guidance to promote the health of examinees.

Free Research Field

先制医療

Academic Significance and Societal Importance of the Research Achievements

様々な機械学習モデルを疫学データに活用する事で、疫学データへの人工知能（機械学習）技術適応の枠組みの検証を行うことが出来た。また、将来の健康診断結果を高精度に予測する機械学習モデルの開発にも成功した。今後は、開発した予測モデルをどのように使うか、研究の枠組みをどのように活用していくかを検討するために、ランダム化比較試験を含めた更なる検討を進めていく。