A multiagent reinforcement learning algorithm for personalized recommendations in bipolar disorder.

This study introduces a novel multiagent reinforcement learning (MARL) algorithm designed for identifying and optimizing personalized recommendations in bipolar disorder. The algorithm leverages longitudinal offline data from wearables to recommend self-care strategies tailored to individual patients. We focus on self-care strategies involving physical activity (measured by steps), sleep duration, and bedtime consistency, aiming to reduce the periods of mood exacerbations. A key innovation of our MARL approach is the integration of copulas to model interagent dependencies, enhancing coordination among agents and improving policy learning. Findings suggest that following our algorithm's self-care recommendations could significantly reduce periods of elevated mood symptoms, resulting in improved overall well-being. Finally, the algorithm offers important clinical insights for treating bipolar patients, and shows promising theoretical properties independent of the specific application. Thus, this work not only advances MARL applications in personalized healthcare but also provides a new algorithmic approach for adaptive interventions in a wide range of chronic diseases.