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タイトル: Health improvement framework for actionable treatment planning using a surrogate Bayesian model
著者: Nakamura, Kazuki
Kojima, Ryosuke  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0003-1095-8864 (unconfirmed)
Uchino, Eiichiro
Ono, Koh
Yanagita, Motoko  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-0339-9008 (unconfirmed)
Murashita, Koichi
Itoh, Ken
Nakaji, Shigeyuki
Okuno, Yasushi  KAKEN_id
著者名の別形: 中村, 和貴
小島, 諒介
内野, 詠一郎
尾野, 亘
柳田, 素子
村下, 公一
伊東, 健
中路, 重之
奥野, 恭史
キーワード: Computer science
Machine learning
Preventive medicine
発行日: 2021
出版者: Springer Nature
誌名: Nature Communications
巻: 12
論文番号: 3088
抄録: Clinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been the primary concern of diagnosis support according to comprehensive patient information. A prominent issue is the development of objective treatment processes in clinical situations. This study proposes a framework to plan treatment processes in a data-driven manner. A key point of the framework is the evaluation of the actionability for personal health improvements by using a surrogate Bayesian model in addition to a high-performance nonlinear ML model. We first evaluate the framework from the viewpoint of its methodology using a synthetic dataset. Subsequently, the framework is applied to an actual health checkup dataset comprising data from 3132 participants, to lower systolic blood pressure and risk of chronic kidney disease at the individual level. We confirm that the computed treatment processes are actionable and consistent with clinical knowledge for improving these values. We also show that the improvement processes presented by the framework can be clinically informative. These results demonstrate that our framework can contribute toward decision-making in the medical field, providing clinicians with deeper insights.
記述: 効果的な健康改善プランを提案するAIを開発 --個別化医療における健康介入への活用に期待--. 京都大学プレスリリース. 2021-05-28.
著作権等: © The Author(s) 2021
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
URI: http://hdl.handle.net/2433/263138
DOI(出版社版): 10.1038/s41467-021-23319-1
PubMed ID: 34035243
関連リンク: https://www.kyoto-u.ac.jp/ja/research-news/2021-05-28-1
出現コレクション:学術雑誌掲載論文等

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