Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics
This addresses the need for efficient and personalized therapeutic recommendations for mental health professionals, though it is incremental as it applies existing DRL methods to a new domain.
The paper tackles the problem of generating personalized topic recommendations for therapists in psychotherapy by developing a Reinforcement Learning AI Companion that creates multi-objective policies for four psychiatric conditions, achieving results that capture historical therapist data with varying best models per disorder and rating scale.
We introduce a Reinforcement Learning Psychotherapy AI Companion that generates topic recommendations for therapists based on patient responses. The system uses Deep Reinforcement Learning (DRL) to generate multi-objective policies for four different psychiatric conditions: anxiety, depression, schizophrenia, and suicidal cases. We present our experimental results on the accuracy of recommended topics using three different scales of working alliance ratings: task, bond, and goal. We show that the system is able to capture the real data (historical topics discussed by the therapists) relatively well, and that the best performing models vary by disorder and rating scale. To gain interpretable insights into the learned policies, we visualize policy trajectories in a 2D principal component analysis space and transition matrices. These visualizations reveal distinct patterns in the policies trained with different reward signals and trained on different clinical diagnoses. Our system's success in generating DIsorder-Specific Multi-Objective Policies (DISMOP) and interpretable policy dynamics demonstrates the potential of DRL in providing personalized and efficient therapeutic recommendations.