Machine-Learning-Based Prediction of Client Distress From Session Recordings

Natural language processing (NLP) is a subfield of machine learning that may facilitate the evaluation of therapist-client interactions and provide feedback to therapists on client outcomes on a large scale. However, there have been limited studies applying NLP models to client-outcome prediction that have (a) used transcripts of therapist-client interactions as direct predictors of client-symptom improvement, (b) accounted for contextual linguistic complexities, and (c) used best practices in classical training and test splits in model development. Using 2,630 session recordings from 795 clients and 56 therapists, we developed NLP models that directly predicted client symptoms of a given session based on session recordings of the previous session (Spearman's rho = .32, p < .001). Our results highlight the potential for NLP models to be implemented in outcome-monitoring systems to improve quality of care. We discuss implications for future research and applications.