ArticleNPJ digital medicine2025
Optimizing long term disease prevention with reinforcement learning: a framework for precision lipid control.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Equity-Preserving Public Health Resource Allocation Using Multi-Objective Safe Reinforcement Learning: Evidence from Thailand.International journal of environmental research and public health · 2026Article
- Optimization of glucocorticoid administration in patients with sepsis using reinforcement learning: a multicenter retrospective study.Frontiers in cellular and infection microbiology · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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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.
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Registered trials
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