Reinforcement Learning for Personalized Treatment Recommendation

Abstract

Personalized treatment recommendation is a critical aspect of modern healthcare, aiming to tailor medical interventions based on individual patient characteristics and responses. This paper presents a Reinforcement Learning-based framework for Personalized Treatment Recommendation that learns optimal treatment policies through interaction with patient data. The proposed system models the treatment process as a sequential decision-making problem, where an agent selects actions to maximize long-term patient outcomes. Reinforcement learning algorithms such as Q-learning and deep reinforcement learning are employed to capture dynamic treatment strategies and adapt to varying patient conditions. The framework incorporates patient history, clinical parameters, and treatment responses to generate personalized recommendations. Additionally, integration with prior research in machine learning and healthcare analytics enhances the robustness and generalization capability of the system. Experimental results demonstrate that the proposed approach improves treatment effectiveness and decision-making compared to traditional rule-based methods. The study highlights the potential of reinforcement learning in enabling adaptive, data-driven, and patient-centric treatment strategies in healthcare systems.