Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression

• Published in: Journal of affective disorders Vol. 230; pp. 84 - 86
• Main Authors: Carrillo, Facundo, Sigman, Mariano, Fernández Slezak, Diego, Ashton, Philip, Fitzgerald, Lily, Stroud, Jack, Nutt, David J., Carhart-Harris, Robin L.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2018
• Subjects: Computational psychiatry; Depression; Machine learning; Natural speech analysis; Predict therapeutic effectiveness; Psilocybin treatment; Treatment-resistant depression; Natural speech analysis; Computational psychiatry; Machine learning; Treatment-resistant depression; Depression; Psilocybin treatment; Predict therapeutic effectiveness
• ISSN: 0165-0327; 1573-2517; 1573-2517
• DOI: 10.1016/j.jad.2018.01.006

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. 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. 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). 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. The sample size was small and replication is required to strengthen inferences on these results. •Interview data from 17 patients with treatment-resistant depression was recorded.•Automated Emotional Analysis, a natural language processing method, was used to quantify emotional content of baseline interviews.•A machine learning algorithm was used to identify patterns in emotional analysis results.•Detected patterns predict therapeutic effectiveness of psilocybin for treatment-resistant depression.