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 Nutt, Robin Carhart-Harris
J Affect Disord April 1, 2018 DOI: 10.1016/j.jad.2018.01.006 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort Peer reviewed |
|---|---|
| Population | Patients with treatment-resistant depression |
| Intervention | Psilocybin |
| Topics | Psychedelic-assisted therapy Depression Psilocybin |
| Keywords | Psilocybin therapy Psilocybin treatment Depression treatment Mental health treatment Speech biomarkers Speech analysis Vocal patterns Voice analysis Linguistic features Acoustic markers Speech features Treatment prediction Predictive analytics Outcome forecasting Prognosis Patient response Personalized medicine Precision medicine Algorithms for depression Digital psychiatry |
| Citations | 57 |
| Key findings | 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.