Reinforcement Learning in Personalized Medicine: Tailoring Treatment Protocols to Individual Patients

Abstract

Personalized medicine goal is to provide treatment plans that are unique to each patient; however, barriers like fairness, interpretability, and heterogeneity hinder the development of the approach. To overcome these drawbacks, this work proposes a fairness-aware hybrid RL model to enhance the clinical decision-making process. The proposed framework integrates the model-based and model-free RL to achieve short-term patient stabilization and long-term patient recovery. With the help of the fairness-aware reward adjustments, the model minimizes demographic gaps, obtaining a low bias index of 0.05 while keeping an action accuracy of 88.5%. To achieve this, SHAP (SHapley Additive exPlanations) is used to make the model more interpretable and explain to the clinicians what features are important in the treatment process like heart rate and lactate levels to build trust. The framework generates a higher cumulative reward than the standalone RL models (+1500 vs. +1350 and +1200 for model-free and model-based RL, respectively) and can be fine-tuned for different patient populations. The results confirm the model’s ability to prioritize fair and explainable healthcare solutions when tested with the MIMIC-III dataset. This work closes the gap between theory and practice, and opens the path towards large-scale, responsible, and learning from data AI-Powered Precision Medicine. The future work is to integrate with clinical processes in real-time and broaden the fairness criteria to account for multiple forms of discrimination.