Health improvement framework for actionable treatment planning using a surrogate Bayesian model

• 中村, 和貴

  Research and Business Development Department, Kyowa Hakko Bio Co., Ltd.; Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University

• 小島, 諒介

  Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University

• 内野, 詠一郎

  Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University

• 尾野, 亘

  Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University

• 柳田, 素子

  Department of Nephrology, Graduate School of Medicine, Kyoto University; Institute for the Advanced Study of Human Biology, Kyoto University

• 村下, 公一

  Center of Innovation Research Initiatives Organization, Hirosaki University

• 伊東, 健

  Department of Stress Response Science, Hirosaki University Graduate School of Medicine

• 中路, 重之

  Department of Social Health, Hirosaki University Graduate School of Medicine

• 奥野, 恭史

  Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University

抄録

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.

収録刊行物

• Nature Communications

  Nature Communications 12 2021

  Springer Nature

キーワード

• Computer science
• Machine learning
• Preventive medicine

詳細情報

• CRID: 1050569767912395264
• NII論文ID: 120007041705
• DOI: 10.1038/s41467-021-23319-1
• HANDLE: 2433/263138
• ISSN: 20411723
• Web Site: https://www.nature.com/articles/s41467-021-23319-1.pdf
• Web Site: https://www.nature.com/articles/s41467-021-23319-1
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN