SCH: Advancing Public Health Intervention with Data-driven Restless Multi-Armed Bandit Framework - Public health interventions are essential in mitigating the burden of both infectious and chronic diseases, including diabetes and cardiovascular conditions. Early interventions, such as timely medication, lifestyle modifications, and the dissemination of health information, can significantly improve health outcomes. However, optimizing these interventions remains a major challenge due to limited healthcare resources, patient heterogeneity, and the complex interplay of disease phenotypes and social determinants. Identifying high-risk populations and efficiently allocating healthcare resources requires advanced computational approaches that integrate real-world data and adaptive decision-making frameworks. This proposal focuses on developing data-driven restless multi-armed bandit (RMAB) techniques to optimize intervention strategies for diabetes, cardiovascular conditions, and maternal health in the U.S. healthcare system. Due to the complex nature of these diseases, we propose a new contextual RMAB model that integrates patient context and datasets, including observational EHR data (e.g., MIMIC-III, MIMIC-IV, All of Us, and MGH Biobanks) and intervention trials (e.g., REAL HEALTH-Diabetes, Look Ahead, DPP, mMitra in maternal health), to allocate interventions based on disease progression and patient responses. We also propose a network RMAB model to handle socioeconomic factors within a social network and a multi-agent RMAB model to optimize intervention strategies in decentralized healthcare providers. These models will enable more precise and efficient allocation of interventions by incorporating patient connectivity, healthcare facility constraints, and policy-driven incentives, bridging the gap between computational decision-making and real-world healthcare applications. By leveraging machine learning, network analysis, and health economics, this proposal will develop scalable and interpretable AI-driven frameworks for optimizing healthcare resource allocation. Collaborating with hospitals, universities, non-profit organizations, and government agencies, this project will ensure that research-driven innovations transition effectively into clinical practice, facilitating evidence-based decision-making to support intervention and improve patient outcomes at scale. RELEVANCE (See instructions): This proposal aligns with the mission of the NHLBI by advancing data-driven decision-support solutions to optimize healthcare resource allocation based on patient health records and social determinants. Focusing on diabetes, cardiovascular conditions, and maternal health, we use real-world observational data and existing interventional trials to improve risk stratification and intervention recommendations in resource-limited settings, translating AI research to clinical practice to enhance population health at scale.
