Grant: Discovery Launch Supplement

Introduction: Reinforcement learning (RL) represents a transformative paradigm in the realm of machine learning, offering a dynamic approach to decision-making and optimization, distinct from traditional supervised and unsupervised methods. Our innovative approach, Longitudinal Adaptive Risk-Aware Reinforcement Learning (L-ARRL), takes RL to a new level by addressing decision-making scenarios characterized by multiple risks. Unlike classical RL strategies that prioritize reward maximization without considering evolving multi-risk factors, L-ARRL introduces a mechanism for incorporating risk awareness longitudinally into the decision-making process. This paradigm shift is crucial, particularly in real-world applications where a blind pursuit of rewards can lead to undesirable outcomes. L-ARRL's primary innovation lies in its adaptability, multi-risk assessment, and the ability to provide interpretable decision support. Objectives: The first aim of our project is to create an L-ARRL model that thrives in dynamic environments, effectively balances rewards and multiple risks and offers continuous risk assessment. This model signifies a significant breakthrough in reinforcement learning, with applications extending from personalized medication regimens to equitable resource allocation in various domains. Our second objective centers on demystifying the often-opaque nature of ML models by making L-ARRL predictions more interpretable. Through the integration of attention mechanisms, we aim to shed light on the factors influencing decisions, facilitating validation by experts. Additionally, our goal is to seamlessly integrate the model into a user-friendly web application, providing real-world benefits by assisting in complex decision-making processes. Novelty and Significance: The distinctive features of L-ARRL, including adaptability, multi-risk assessment, and enhanced interpretability, mark a significant advancement in reinforcement learning. Its applications span various domains, offering a bridge between system development and real-world implementation. Our Longitudinal Adaptive Risk-Aware Reinforcement Learning (L-ARRL) approach revolutionizes decision-making in complex, evolving, and risk-laden longitudinal data scenarios. Unlike conventional reinforcement learning, which often prioritizes reward maximization at the expense of real-time risk considerations, L-ARRL introduces a novel mechanism for personalized, risk-aware decision-making over time. This shift addresses critical real-world concerns where a sole focus on rewards can yield undesirable outcomes. L-ARRL's key innovation lies in its ability to provide transparent and adaptable solutions, enhancing decision-making across diverse domains, with healthcare as just one example. By tailoring treatment strategies to individual needs in dynamic healthcare settings, L-ARRL has the potential to promote fairness and responsive healthcare decisions.