Reinforcement Learning for Personalized Treatment Strategies: A Data-Driven Approach to Optimizing Patient Care
Abstract
Reinforcement learning (RL) has the potential to transform personalized medicine by dynamically adapting treatment plans based on patient responses. This paper reviews key RL algorithms, such as Q-learning, Deep Q Networks (DQN), and Actor-Critic methods, applied in optimizing chemotherapy regimens, diabetes management, and sepsis treatment. The study examines challenges, including reward function design, sample efficiency, and real-world clinical implementation barriers. Future research directions for integrating RL with electronic health records (EHRs) and real-time patient monitoring systems to improve healthcare outcomes are also discussed.
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