Health improvement framework for planning actionable treatment process using surrogate Bayesian model
This work addresses the need for data-driven treatment planning in healthcare, providing clinicians with actionable insights, though it appears incremental as it builds on existing ML methods for a specific medical application.
The study tackled the problem of developing objective treatment processes in clinical situations by proposing a novel framework that uses a surrogate Bayesian model to evaluate actionability for personal health improvements, applied to a dataset of 3,132 participants to improve systolic blood pressure values at the individual level.
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.