Facundo Carrillo1, Mariano Sigman2, Diego Fernández Slezak3, Philip Ashton4, Lily Fitzgerald4, Jack Stroud4, David J Nutt4, Robin L Carhart-Harris4. 1. Applied Artificial Intelligence Lab, Computer Science Department, School of Science, Buenos Aires University, CONICET, Buenos Aires 1428, Argentina; CONICET-Universidad de Buenos Aires, Instituto de Investigación en Ciencias de la Computación (ICC), Buenos Aires, Argentina. Electronic address: fcarrillo@dc.uba.ar. 2. Integrative Neuroscience Lab, Universidad Torcuato Di Tella, CONICET, Buenos Aires 1428, Argentina. 3. Applied Artificial Intelligence Lab, Computer Science Department, School of Science, Buenos Aires University, CONICET, Buenos Aires 1428, Argentina; CONICET-Universidad de Buenos Aires, Instituto de Investigación en Ciencias de la Computación (ICC), Buenos Aires, Argentina. 4. Psychedelic Research Group, Centre for Psychiatry, Dept of Medicine, Imperial College London, London, UK.
Abstract
BACKGROUND: Natural speech analytics has seen some improvements over recent years, and this has opened a window for objective and quantitative diagnosis in psychiatry. Here, we used a machine learning algorithm applied to natural speech to ask whether language properties measured before psilocybin for treatment-resistant can predict for which patients it will be effective and for which it will not. METHODS: A baseline autobiographical memory interview was conducted and transcribed. Patients with treatment-resistant depression received 2 doses of psilocybin, 10 mg and 25 mg, 7 days apart. Psychological support was provided before, during and after all dosing sessions. Quantitative speech measures were applied to the interview data from 17 patients and 18 untreated age-matched healthy control subjects. A machine learning algorithm was used to classify between controls and patients and predict treatment response. RESULTS: Speech analytics and machine learning successfully differentiated depressed patients from healthy controls and identified treatment responders from non-responders with a significant level of 85% of accuracy (75% precision). CONCLUSIONS: Automatic natural language analysis was used to predict effective response to treatment with psilocybin, suggesting that these tools offer a highly cost-effective facility for screening individuals for treatment suitability and sensitivity. LIMITATIONS: The sample size was small and replication is required to strengthen inferences on these results.
BACKGROUND: Natural speech analytics has seen some improvements over recent years, and this has opened a window for objective and quantitative diagnosis in psychiatry. Here, we used a machine learning algorithm applied to natural speech to ask whether language properties measured before psilocybin for treatment-resistant can predict for which patients it will be effective and for which it will not. METHODS: A baseline autobiographical memory interview was conducted and transcribed. Patients with treatment-resistant depression received 2 doses of psilocybin, 10 mg and 25 mg, 7 days apart. Psychological support was provided before, during and after all dosing sessions. Quantitative speech measures were applied to the interview data from 17 patients and 18 untreated age-matched healthy control subjects. A machine learning algorithm was used to classify between controls and patients and predict treatment response. RESULTS: Speech analytics and machine learning successfully differentiated depressedpatients from healthy controls and identified treatment responders from non-responders with a significant level of 85% of accuracy (75% precision). CONCLUSIONS: Automatic natural language analysis was used to predict effective response to treatment with psilocybin, suggesting that these tools offer a highly cost-effective facility for screening individuals for treatment suitability and sensitivity. LIMITATIONS: The sample size was small and replication is required to strengthen inferences on these results.
Authors: Alexandra König; Elisa Mallick; Johannes Tröger; Nicklas Linz; Radia Zeghari; Valeria Manera; Philippe Robert Journal: Eur Psychiatry Date: 2021-10-13 Impact factor: 5.361
Authors: Eline C H M Haijen; Mendel Kaelen; Leor Roseman; Christopher Timmermann; Hannes Kettner; Suzanne Russ; David Nutt; Richard E Daws; Adam D G Hampshire; Romy Lorenz; Robin L Carhart-Harris Journal: Front Pharmacol Date: 2018-11-02 Impact factor: 5.810