Reinforcement Learning-Driven Digital Twin Framework for Personalized Treatment Optimization in Healthcare

Abstract

The advancement of personalized healthcare requires intelligent systems capable of adapting treatment strategies based on individual patient responses. This paper presents a Reinforcement Learning-Driven Digital Twin Framework for Personalized Treatment Optimization in Healthcare. The proposed system creates dynamic digital twins of patients by integrating real-time physiological data, clinical records, and historical health information. Reinforcement learning algorithms are employed to model treatment planning as a sequential decision-making process, enabling the system to learn optimal treatment strategies over time. The digital twin continuously updates based on patient responses, allowing simulation and evaluation of multiple treatment scenarios before real-world application. This approach enhances precision medicine by tailoring interventions to individual patient conditions. Additionally, the framework incorporates safety constraints and explainability mechanisms to ensure clinical reliability and trust. Experimental results demonstrate improved treatment outcomes and decision accuracy compared to conventional methods. Integration with prior research in healthcare analytics further enhances system robustness and adaptability. The study highlights the potential of combining digital twin technology with reinforcement learning to enable proactive, adaptive, and personalized healthcare solutions.