Deep Reinforcement Learning for Personalized Clinical Decision Support

Clinical decision making is inherently sequential and must consider the complex, high-dimensional, and rapidly changing physiological state of each patient. Traditional supervised models provide static risk predictions, but they cannot suggest how to act over time. Deep learning (DL) is skilled at extracting concise representations from multimodal electronic health record (EHR) data, while reinforcement learning (RL) optimizes sequences of actions to maximize long-term rewards. Their combination—deep reinforcement learning (DRL)—has become a natural approach for personalized, data-driven treatment planning.This paper presents a comprehensive DRL study in health informatics and is intentionally written with full methodological transparency to ensure reproducibility. After an extensive review of literature covering critical care, oncology, hospital operations, and “safe-RL” techniques, we detail the construction of two benchmarking tasks: (i) fluid-vasopressor titration for septic shock patients in the publicly available MIMIC-IV v3.1 ICU database, and (ii) bed-capacity management in a high-fidelity hospital-operations simulator. A multimodal transformer encoder feeds weighted-dueling–double-DQN (WD-DDQN) and DQN agents trained with Conservative Q-Learning (CQL) regularization. Rigorous offline policy evaluation (OPE) using doubly robust and importance sampling estimators demonstrates that the learned policies reduce estimated 90-day mortality by 10.3% and average length of stay by 10.5% compared to historical clinician behavior—without violating predefined safety constraints. These improvements were statistically significant and supported by rigorous offline policy evaluation using bootstrapped confidence intervals from doubly robust and importance sampling estimators. We conclude by discussing interpretability, fairness, regulatory pathways, and open technical challenges such as causal-RL, continuous learning under drift, and benchmark ecosystems. Our findings reinforce the potential of DRL as a key component of the next generation of clinical decision-support systems.