Reinforcement Learning for Antidepressant Dose Adjustment: An Explainable Agent Approach

Major Depressive Disorder (MDD) is a prevalent psychiatric condition requiring long-term pharmacological management, with escitalopram often prescribed as a first-line treatment. However, optimizing antidepressant dosing remains challenging due to heterogeneous patient responses, complex symptom trajectories, and variable tolerance to side effects. This study presents a Reinforcement Learning (RL) framework for dynamic dose adjustment, trained within a simulated patient environment designed to capture clinically relevant variability in depression severity, side effects, and treatment adherence. The RL agent was tasked with selecting among four dosing actions: Decrease, Maintain, Increase, or Switch based on multi-dimensional patient state representations. An ε-greedy exploration strategy with decaying exploration probability facilitated policy convergence over 30 training episodes. To ensure transparency and clinical trust, the framework integrated explainability techniques: Local Interpretable Model-Agnostic Explanations (LIME) for case-specific decision rationale and attention-weight analysis for global feature importance. Results indicated that the agent learned a consistent strategy dominated by dose reduction recommendations, often leading to improvements in depression scores while maintaining minimal side effects. Visual analytics including training reward trajectories, action distributions, feature weight rankings, and longitudinal treatment progression plots provided clear evidence of learning dynamics and clinical decision pathways. Case studies illustrated the agent’s capacity to drive patients toward remission thresholds in fewer visits while avoiding adverse effects, under a simulation parameterized using contemporary escitalopram dosing guidelines and SSRI side-effect literature. LIME analysis revealed that variables such as high normalized BMI and shorter treatment duration significantly influenced the “Decrease” action, while age and depression severity modulated decision probabilities. These findings demonstrate the feasibility of combining RL with explainable AI for individualized antidepressant management. Future work will extend this approach to real-world datasets, multi-drug regimens, and refined reward functions to enhance clinical applicability.