Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression.
Facundo Carrillo, Mariano Sigman, Diego Fernández Slezak, Philip Ashton, Lily Fitzgerald, Jack Stroud, David J. Nutt, Robin L. Carhart-Harris
J Affect Disord April 1, 2018 DOI: 10.1016/j.jad.2018.01.006 via PubMed
Summary
AI-generated from the abstractAn algorithm analyzing natural speech from baseline interviews can predict which patients with treatment-resistant depression will respond to psilocybin therapy. The approach uses vocal patterns and linguistic features to forecast treatment outcomes, suggesting that speech biomarkers may enable personalized medicine in mental health. This predictive capability could help identify likely responders before treatment begins, advancing precision psychiatry for depression.
Study at a glance
| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Population | Patients with treatment-resistant depression |
| Intervention | Psilocybin |
| Topics | Psychedelic-assisted therapy |
| Keywords | Psilocybin therapy Psilocybin treatment Depression treatment Mental health treatment |
| Citations | 57 |
| Key finding | Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression. |
Abstract
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression.