Personalized decision making for coronary artery disease treatment using offline reinforcement learning.

ABSTRACT: Choosing optimal revascularization strategies for patients with obstructive coronary artery disease (CAD) remains a clinical challenge. While randomized controlled trials offer population-level insights, gaps remain regarding personalized decision-making for individual patients. We applied off-policy reinforcement learning (RL) to a composite data model from 41,328 unique patients with angiography-confirmed obstructive CAD. In an offline setting, we estimated optimal treatment policies and evaluated these policies using weighted importance sampling. Our findings indicate that RL-guided therapy decisions outperformed physician-based decision making, with RL policies achieving up to 32% improvement in expected rewards based on composite major cardiovascular events outcomes. Additionally, we

SUBMITTER: Ghasemi P 

PROVIDER: S-EPMC11825836 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature