Optimizing long term disease prevention with reinforcement learning: a framework for precision lipid control

Abstract

The prevention of chronic disease is a long-term combat with continual fine-tuning to adapt to the course of disease. Without comprehensive insights, prescriptions may prioritize short-term gains but deviate from trajectories toward long-term survival. Here we introduce Duramax, an evidence-based framework empowered by reinforcement learning to optimize long-term preventive strategies. Duramax learned from real-world treatment trajectories involving over 200 lipid-modifying drugs across more than 3.6 million months, becoming specialized in cardiovascular disease (CVD) prevention. Duramax demonstrated a superior performance in model validation using an independent cohort encompassing over 29.7 million treatment months. Specifically, Duramax achieved policy value of 93, outperforming clinicians with value of 68. When clinicians’ decisions aligned with Duramax’s suggestions, CVD risk reduced by 6%. Moreover, post hoc analysis confirmed that Duramax’s decisions were transparent and reasonable. Our research showcases how tailored computational analysis on well-curated health records can achieve high nuance in personalized disease prevention.