Downloads: 47

Files in This Item:
File Description SizeFormat 
dnikk00110.pdfDissertation_全文54.56 MBAdobe PDFView/Open
ynikk00110.pdfAbstract_要旨106.68 kBAdobe PDFView/Open
Title: Health improvement framework for actionable treatment planning using a surrogate Bayesian model
Other Titles: 階層ベイズモデルを利用した実行可能な健康改善プランを提案するAI技術の開発
Authors: Nakamura, Kazuki
Author's alias: 中村, 和貴
Keywords: Precision medicine
Machine learning
Hierarchical Bayesian model
Health improvement
Actionable treatment planning
Issue Date: 23-Mar-2023
Publisher: Kyoto University
Conferring University: 京都大学
Degree Level: 新制・課程博士
Degree Discipline: 博士(人間健康科学)
Degree Report no.: 甲第24539号
Degree no.: 人健博第110号
Conferral date: 2023-03-23
Degree Call no.: 新制||人健||8(附属図書館)
Degree Affiliation: 京都大学大学院医学研究科人間健康科学系専攻
Examination Committee members: (主査)教授 木下 彩栄, 教授 中尾 恵, 教授 中山 健夫
Provisions of the Ruling of Degree: 学位規則第4条第1項該当
Rights: This is an article published in Nature Communications. The final authenticated version is available online at:
DOI: 10.14989/doctor.k24539
Appears in Collections:060_4 Doctoral Dissertation (Human Health Science)

Show full item record

Export to RefWorks

Export Format: 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.