Multimodal Reinforcement Learning for Embedding Networks and Medication Recommendation in Parkinson’s Disease

This study presents a comprehensive framework for analyzing Parkinson’s disease (PD) progression and optimizing medication recommendations based on multimodal data integration and reinforcement learning (RL) techniques. The research framework integrates diverse biomarkers from structured clinical assessments, imaging data, and biospecimen information, creating various single, bimodal, and trimodal datasets. Deep learning-based embedding networks are employed to transform high-dimensional data into lower-dimensional representations, capturing essential patterns related to PD progression. The learned representations are evaluated using visualization methods and predictive modeling to differentiate clinically relevant outcomes. Subsequently, medication recommendation models are developed based on the learned representations, assisting clinicians in making informed decisions to manage PD progression effectively. Optimal medication policies are derived considering different modality settings, revealing insights into medication prescribing tendencies across modalities. The findings highlight the significance of integrating diverse data modalities in understanding and managing PD progression, showcasing the potential of RL-based medication recommendation systems in clinical decision-making. This research contributes to the advancement of personalized medicine strategies for PD patients, emphasizing the importance of tailored medication regimes based on multimodal data insights.