Using natural language processing as a scalable mental status evaluation technique

Abstract

Mental health is in a state of crisis with demand for mental health services significantly surpassing available care. As such, building scalable and objective measurement tools for mental health evaluation is of primary concern. Given the usage of spoken language in diagnostics and treatment, it stands out as potential methodology. Here a model is built for mental health status evaluation using natural language processing. Specifically, a RoBERTa-based model is fine-tuned on text from psychotherapy sessions to predict mental health status with prediction accuracy on par with clinical evaluations at 74%.

Margot Wagner

Postdoctoral Researcher

Interested in the use of data science and AI in mental health and using neuroscience to inspire next generation AI tools.