TITLE:
Reinforcement Learning-Based Personalized Depression Treatment Using Synthetic Data and Real-Time Decision Support
AUTHORS:
Rocco de Filippis, Abdullah Al Foysal
KEYWORDS:
Reinforcement Learning, Depression, Synthetic Data, Real-Time Recommendation, Precision Psychiatry, Deep Q-Learning, Treatment Personalization, Patient Simulation
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.8,
August
27,
2025
ABSTRACT: Depression treatment often involves a complex and lengthy trial-and-error process, where clinicians sequentially prescribe medications to identify the most effective treatment for each patient. This approach can lead to delayed recovery, unnecessary side effects, and increased healthcare costs. To address this challenge, we present a Reinforcement Learning (RL)-based framework designed to optimize antidepressant treatment strategies through dynamic, patient-specific decision-making. The proposed system leverages synthetically generated patient data to simulate real-world treatment scenarios while ensuring privacy and scalability. A synthetic depression patient simulator was developed to model daily symptom trajectories influenced by medication type, dosage, adherence, side effects, and stochastic life events. This simulated data allowed the training of a deep Q-learning agent within a custom-built reinforcement learning environment. The agent learned to recommend treatment adjustments or continuations based on temporal symptom patterns and treatment history. Key components of the framework include experience replay, target model updates, and an epsilon-greedy exploration strategy to balance exploration and exploitation during training. The system was evaluated using unseen synthetic patients to assess generalization performance. Comprehensive visual analyses were conducted to characterize the symptom distribution, medication assignment, agent reward dynamics, and real-time treatment recommendations. The real-time recommendation system demonstrated the ability to provide timely, personalized treatment suggestions, switching medications when appropriate and maintaining stability when patient symptoms improved. The model’s decision-making process is closely aligned with clinical reasoning, supporting its potential as a decision support tool in precision psychiatry. This study offers a privacy-preserving, scalable, and clinically relevant pathway for optimizing depression treatment through reinforcement learning, contributing to the advancement of intelligent mental health care systems.