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

TL;DR

This paper introduces a novel data-driven framework that combines surrogate Bayesian models with machine learning to plan personalized, actionable treatment processes for health improvement, validated on synthetic and real datasets.

Contribution

The study presents a new framework integrating surrogate Bayesian models with ML to evaluate and plan personalized treatments, enhancing decision-making in clinical practice.

Findings

Framework effectively evaluated on synthetic data.

Applied to real health checkup data from 3,132 participants.

Confirmed treatment plans are actionable and align with clinical knowledge.

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. However, the remaining prominent issue is the development of objective treatment processes in clinical situations. This study proposes a novel 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 evaluated the framework from the viewpoint of its methodology using a synthetic dataset. Subsequently, the framework was applied to an actual health checkup dataset comprising data from 3,132 participants, to improve systolic blood pressure values at the individual level. We confirmed that the computed treatment processes are actionable and consistent with clinical knowledge for lowering blood pressure. These results demonstrate that our framework could contribute toward decision making in the medical field, providing clinicians with deeper insights.