Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression
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 f...
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2018
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650327_v230_n_p84_Carrillo http://hdl.handle.net/20.500.12110/paper_01650327_v230_n_p84_Carrillo |
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paper:paper_01650327_v230_n_p84_Carrillo2023-06-08T15:14:37Z Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression Computational psychiatry Depression Machine learning Natural speech analysis Predict therapeutic effectiveness Psilocybin treatment Treatment-resistant depression psilocybine antidepressant agent psilocybine psychedelic agent adult Article autobiographical memory Bayesian learning clinical article clinical outcome controlled clinical trial controlled study depression assessment female human male mental patient open study prediction priority journal psychopharmacotherapy psychosocial care quantitative analysis Quick Inventory of Depressive Symptoms 16 speech analysis treatment resistant depression treatment response algorithm case control study clinical trial episodic memory language machine learning middle aged physiology procedures speech speech analysis treatment resistant depression Adult Algorithms Antidepressive Agents Case-Control Studies Depressive Disorder, Treatment-Resistant Female Hallucinogens Humans Language Machine Learning Male Memory, Episodic Middle Aged Psilocybin Speech Speech Production Measurement 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. © 2018 2018 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650327_v230_n_p84_Carrillo http://hdl.handle.net/20.500.12110/paper_01650327_v230_n_p84_Carrillo |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Computational psychiatry Depression Machine learning Natural speech analysis Predict therapeutic effectiveness Psilocybin treatment Treatment-resistant depression psilocybine antidepressant agent psilocybine psychedelic agent adult Article autobiographical memory Bayesian learning clinical article clinical outcome controlled clinical trial controlled study depression assessment female human male mental patient open study prediction priority journal psychopharmacotherapy psychosocial care quantitative analysis Quick Inventory of Depressive Symptoms 16 speech analysis treatment resistant depression treatment response algorithm case control study clinical trial episodic memory language machine learning middle aged physiology procedures speech speech analysis treatment resistant depression Adult Algorithms Antidepressive Agents Case-Control Studies Depressive Disorder, Treatment-Resistant Female Hallucinogens Humans Language Machine Learning Male Memory, Episodic Middle Aged Psilocybin Speech Speech Production Measurement |
| spellingShingle |
Computational psychiatry Depression Machine learning Natural speech analysis Predict therapeutic effectiveness Psilocybin treatment Treatment-resistant depression psilocybine antidepressant agent psilocybine psychedelic agent adult Article autobiographical memory Bayesian learning clinical article clinical outcome controlled clinical trial controlled study depression assessment female human male mental patient open study prediction priority journal psychopharmacotherapy psychosocial care quantitative analysis Quick Inventory of Depressive Symptoms 16 speech analysis treatment resistant depression treatment response algorithm case control study clinical trial episodic memory language machine learning middle aged physiology procedures speech speech analysis treatment resistant depression Adult Algorithms Antidepressive Agents Case-Control Studies Depressive Disorder, Treatment-Resistant Female Hallucinogens Humans Language Machine Learning Male Memory, Episodic Middle Aged Psilocybin Speech Speech Production Measurement Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression |
| topic_facet |
Computational psychiatry Depression Machine learning Natural speech analysis Predict therapeutic effectiveness Psilocybin treatment Treatment-resistant depression psilocybine antidepressant agent psilocybine psychedelic agent adult Article autobiographical memory Bayesian learning clinical article clinical outcome controlled clinical trial controlled study depression assessment female human male mental patient open study prediction priority journal psychopharmacotherapy psychosocial care quantitative analysis Quick Inventory of Depressive Symptoms 16 speech analysis treatment resistant depression treatment response algorithm case control study clinical trial episodic memory language machine learning middle aged physiology procedures speech speech analysis treatment resistant depression Adult Algorithms Antidepressive Agents Case-Control Studies Depressive Disorder, Treatment-Resistant Female Hallucinogens Humans Language Machine Learning Male Memory, Episodic Middle Aged Psilocybin Speech Speech Production Measurement |
| description |
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. © 2018 |
| title |
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression |
| title_short |
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression |
| title_full |
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression |
| title_fullStr |
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression |
| title_full_unstemmed |
Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression |
| title_sort |
natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression |
| publishDate |
2018 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650327_v230_n_p84_Carrillo http://hdl.handle.net/20.500.12110/paper_01650327_v230_n_p84_Carrillo |
| _version_ |
1768545732926636032 |