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

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 fr

SUBMITTER: Nakamura K 

PROVIDER: S-EPMC8149666 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature