Explainable Reinforcement Learning Framework for Personalized Treatment Recommendation in Healthcare Systems

Abstract

Personalized treatment planning in healthcare requires intelligent systems capable of adapting to patient-specific conditions while maintaining transparency in decision-making. This paper presents an Explainable Reinforcement Learning Framework for Personalized Treatment Recommendation in Healthcare Systems. The proposed approach models treatment planning as a sequential decision-making problem, where a reinforcement learning agent learns optimal policies based on patient states and treatment outcomes. Deep reinforcement learning techniques, including policy gradient methods and deep Q-networks, are employed to handle complex and high-dimensional healthcare data. To address the lack of interpretability in reinforcement learning models, explainable AI techniques are integrated to provide insights into decision pathways and feature contributions. The framework incorporates safety constraints to ensure clinically valid recommendations and reduce the risk of harmful interventions. Multi-modal patient data, including electronic health records and real-time monitoring data, are utilized to enhance decision accuracy. Experimental results demonstrate improved treatment outcomes and adaptability compared to traditional static models. Integration with prior research in healthcare analytics strengthens system robustness and generalization. The study highlights the potential of explainable reinforcement learning in enabling adaptive, transparent, and patient-centric treatment recommendation systems.