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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-05-25

AUTHORS

Kazuki Nakamura, Ryosuke Kojima, Eiichiro Uchino, Koh Ono, Motoko Yanagita, Koichi Murashita, Ken Itoh, Shigeyuki Nakaji, Yasushi Okuno

ABSTRACT

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. More... »

PAGES

3088

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41467-021-23319-1

    DOI

    http://dx.doi.org/10.1038/s41467-021-23319-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1138324528

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/34035243


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