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Clinical decision making regarding treatments based on personalcharacteristics leads to effective health improvements. Machine learning (ML)has been the primary concern of diagnosis support according to comprehensivepatient information. However, the remaining prominent issue is the developmentof objective treatment processes in clinical situations. This study proposes anovel framework to plan treatment processes in a data-driven manner. A keypoint of the framework is the evaluation of the "actionability" for personalhealth improvements by using a surrogate Bayesian model in addition to ahigh-performance nonlinear ML model. We first evaluated the framework from theviewpoint of its methodology using a synthetic dataset. Subsequently, theframework was applied to an actual health checkup dataset comprising data from3,132 participants, to improve systolic blood pressure values at the individuallevel. We confirmed that the computed treatment processes are actionable andconsistent with clinical knowledge for lowering blood pressure. These resultsdemonstrate that our framework could contribute toward decision making in themedical field, providing clinicians with deeper insights.

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