Reinforcement Learning-Driven Clinical Decision Support for Personalized Treatment Planning

Abstract

The emergence of Reinforcement Learning (RL) has significantly transformed decision-making frameworks in dynamic and uncertain environments. In healthcare, the complexity of patient variability, treatment heterogeneity, and outcome uncertainty makes RL particularly promising for clinical decision support systems (CDSS). This paper presents a Reinforcement Learning-Driven framework for personalized treatment planning that dynamically adapts to patient states and clinical objectives. The proposed system models the treatment process as a Markov Decision Process (MDP), where the agent learns optimal treatment policies through interaction with simulated and historical patient data. A policy network is trained via Deep Q-Learning and Proximal Policy Optimization (PPO) to balance exploration and exploitation, ensuring adaptive yet safe decision recommendations. Experimental evaluations on public clinical datasets demonstrate that the proposed RL-based CDSS outperforms conventional rule-based and supervised learning baselines in achieving higher cumulative rewards, improved patient outcome prediction accuracy, and faster policy convergence. The findings highlight the potential of RL in facilitating precision medicine, enabling individualized treatment optimization while maintaining interpretability and ethical compliance in clinical contexts.