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

J Affect Disord. 2018 Apr 1;230:84-86. doi: 10.1016/j.jad.2018.01.006.


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.

Keywords: Computational psychiatry; Depression; Machine learning; Natural speech analysis; Predict therapeutic effectiveness; Psilocybin treatment; Treatment-resistant depression.

Publication types

  • Clinical Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Algorithms*
  • Antidepressive Agents / therapeutic use*
  • Case-Control Studies
  • Depressive Disorder, Treatment-Resistant / drug therapy*
  • Female
  • Hallucinogens / therapeutic use*
  • Humans
  • Language
  • Machine Learning
  • Male
  • Memory, Episodic
  • Middle Aged
  • Psilocybin / therapeutic use*
  • Speech / physiology
  • Speech Production Measurement / methods*


  • Antidepressive Agents
  • Hallucinogens
  • Psilocybin