Journal of affective disorders
April 1, 2023
Nadav Liam Modlin, Tammy M Miller, James J Rucker et al.
30 citations
Psilocybin therapies show promise for conditions like major depressive disorder, end-of-life anxiety, and obsessive-compulsive disorder. However, little attention has been paid to intrapersonal and interpersonal factors that influence a patient's readiness for such interventions. This paper proposes that readiness assessment should include both intrapersonal and interpersonal factors to improve safety, patient care, and treatment outcomes. Although no reliable and valid instrument currently exists, the authors suggest three areas of focus—patient presentation, therapeutic alliance, and patient safety—to establish readiness and optimize and personalize psilocybin therapy.
Psychopharmacology
August 22, 2023
Robert F. Dougherty, Patrick Clarke, Merve Atli et al.
21 citations
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.
September 30, 2022
Robert F. Dougherty, Patrick Clarke, Merve Alti et al.
3 citations
preprint
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.