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

Clinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Here, the authors introduce a modeling framework to evaluate the actionability of treatment pathways.

Bibliographic Details
Main Authors:	Kazuki Nakamura, Ryosuke Kojima, Eiichiro Uchino, Koh Ono, Motoko Yanagita, Koichi Murashita, Ken Itoh, Shigeyuki Nakaji, Yasushi Okuno
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-05-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-021-23319-1

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spelling	doaj-8121b781a8ec44169977c8ba8efbdc572021-05-30T11:13:52ZengNature Publishing GroupNature Communications2041-17232021-05-0112111510.1038/s41467-021-23319-1Health improvement framework for actionable treatment planning using a surrogate Bayesian modelKazuki Nakamura0Ryosuke Kojima1Eiichiro Uchino2Koh Ono3Motoko Yanagita4Koichi Murashita5Ken Itoh6Shigeyuki Nakaji7Yasushi Okuno8Research and Business Development Department, Kyowa Hakko Bio Co., Ltd.Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto UniversityDepartment of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto UniversityDepartment of Cardiovascular Medicine, Graduate School of Medicine, Kyoto UniversityDepartment of Nephrology, Graduate School of Medicine, Kyoto UniversityCenter of Innovation Research Initiatives Organization, Hirosaki UniversityDepartment of Stress Response Science, Hirosaki University Graduate School of MedicineDepartment of Social Health, Hirosaki University Graduate School of MedicineDepartment of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto UniversityClinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Here, the authors introduce a modeling framework to evaluate the actionability of treatment pathways.https://doi.org/10.1038/s41467-021-23319-1
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Kazuki Nakamura Ryosuke Kojima Eiichiro Uchino Koh Ono Motoko Yanagita Koichi Murashita Ken Itoh Shigeyuki Nakaji Yasushi Okuno
spellingShingle	Kazuki Nakamura Ryosuke Kojima Eiichiro Uchino Koh Ono Motoko Yanagita Koichi Murashita Ken Itoh Shigeyuki Nakaji Yasushi Okuno Health improvement framework for actionable treatment planning using a surrogate Bayesian model Nature Communications
author_facet	Kazuki Nakamura Ryosuke Kojima Eiichiro Uchino Koh Ono Motoko Yanagita Koichi Murashita Ken Itoh Shigeyuki Nakaji Yasushi Okuno
author_sort	Kazuki Nakamura
title	Health improvement framework for actionable treatment planning using a surrogate Bayesian model
title_short	Health improvement framework for actionable treatment planning using a surrogate Bayesian model
title_full	Health improvement framework for actionable treatment planning using a surrogate Bayesian model
title_fullStr	Health improvement framework for actionable treatment planning using a surrogate Bayesian model
title_full_unstemmed	Health improvement framework for actionable treatment planning using a surrogate Bayesian model
title_sort	health improvement framework for actionable treatment planning using a surrogate bayesian model
publisher	Nature Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2021-05-01
description	Clinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Here, the authors introduce a modeling framework to evaluate the actionability of treatment pathways.
url	https://doi.org/10.1038/s41467-021-23319-1
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Health improvement framework for actionable treatment planning using a surrogate Bayesian model

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