A Reinforcement Learning Framework for Real-Time Personalized Treatment Planning in Clinical Environments

Abstract

This paper presents a Reinforcement Learning (RL) framework for real-time, personalized healthcare, aiming to optimize the treatment strategies for individual patients using longitudinal clinical data. The system models the patient-treatment environment as a Partially Observable Markov Decision Process (POMDP), allowing decision-making under uncertainty while integrating multimodal patient information, including Electronic Health Records (EHRs), lab tests, and imaging data. A deep policy network, trained through Proximal Policy Optimization (PPO), dynamically chooses the optimal interventions by balancing the long-term clinical outcomes, risks, costs, and adherence to medical guidelines. The framework combines a model-based simulator for off-policy data augmentation, auxiliary risk predictors to enhance the safety-aware optimization, and interpretable mechanisms to facilitate the clinician trust. Evaluated on more than 50,000 patient records and simulated environments, the proposed model surpassed the existing methods in accuracy, F1-score, Receiver Operating Characteristic-Area Under the Curve (ROC-AUC), and treatment efficiency. Specifically, it achieved 93.6% accuracy and a 0.937 F1-score while reducing the treatment cycles and enhancing safety compliance. These findings highlight the potential of RL to offer adaptive and interpretable decision support in clinical settings, although more real-world testing is necessary to confirm this result.

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