A machine learning model that analyzes language from therapy sessions can predict which patients with treatment-resistant depression will respond to psilocybin therapy. Researchers used a zero-shot classifier based on the BART large language model to measure sentiment (valence and arousal) in transcripts of therapist-patient conversations one day after COMP360 psilocybin administration. These sentiment scores, combined with the Emotional Breakthrough Index and treatment arm, were fed into multinomial logistic regression models. The models predicted responder status at week 3 and through week 12 with 85% and 88% accuracy, respectively, and AUC values of 88% and 85%. This approach could enable early identification of patients needing alternative treatments.
A machine learning model that analyzes language from therapy sessions accurately predicted which patients with treatment-resistant depression would respond to psilocybin treatment. Transcripts of psychological support sessions held one day after COMP360 (a synthetic psilocybin formulation) administration were analyzed using a zero-shot classifier based on the BART large language model to measure sentiment (valence and arousal) for both participant and therapist. These scores, combined with the Emotional Breakthrough Index and treatment arm, were used to predict treatment outcome measured by MADRS scores. Two multinomial logistic regression models predicted responder status at week 3 and through week 12 with 85% and 88% accuracy, and AUC values of 88% and 85%, respectively. The approach enables rapid prognostication of personalized response to psilocybin treatment and insights into therapeutic model optimization.