Artículo
Carrillo, F.; Sigman, M.; Fernández Slezak, D.; Ashton, P.; Fitzgerald, L.; Stroud, J.; Nutt, D.J.; Carhart-Harris, R.L. "Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression" (2018) Journal of Affective Disorders. 230:84-86
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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. © 2018
Registro:
| Documento: | Artículo |
| Título: | Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression |
| Autor: | Carrillo, F.; Sigman, M.; Fernández Slezak, D.; Ashton, P.; Fitzgerald, L.; Stroud, J.; Nutt, D.J.; Carhart-Harris, R.L. |
| Filiación: | 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 Integrative Neuroscience Lab, Universidad Torcuato Di Tella, CONICET, Buenos Aires, 1428, Argentina Psychedelic Research Group, Centre for Psychiatry, Dept of Medicine, Imperial College London, London, United Kingdom |
| Palabras clave: | 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 |
| Año: | 2018 |
| Volumen: | 230 |
| Página de inicio: | 84 |
| Página de fin: | 86 |
| DOI: | http://dx.doi.org/10.1016/j.jad.2018.01.006 |
| Título revista: | Journal of Affective Disorders |
| Título revista abreviado: | J. Affective Disord. |
| ISSN: | 01650327 |
| CODEN: | JADID |
| CAS: | psilocybine, 520-52-5; Antidepressive Agents; Hallucinogens; Psilocybin |
| Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01650327_v230_n_p84_Carrillo |
Referencias:
- Bedi, G., Automated analysis of free speech predicts psychosis onset in high-risk youths (2015) NPJ Schizophr., 1, p. 15030
- Cambria, E., White, B., Jumping nlp curves: a review of natural language processing research (2014) IEEE Comput. Intell. Mag., 9, pp. 48-57
- Carhart-Harris, R.L., Goodwin, G.M., The therapeutic potential of psychedelic drugs: past, present and future (2017) Neuropsychopharmacology
- Carhart-Harris, R.L., Psilocybin with psychological support for treatment-resistant depression: an open-label feasibility study (2016) Lancet Psychiatry, 3, pp. 619-627
- Carrillo, F., (2014), Automated speech analysis for psychosis evaluation. In: International Workshop on Machine Learning and Interpretation in Neuroimaging, 31-39 (Springer International Publishing); Carrillo, F., Cecchi, G.A., Sigman, M., Slezak, D.F., Fast distributed dynamics of semantic networks via social media (2015) Comput. Intell. Neurosci., 2015 (50)
- Carrillo, F., (2016), Emotional intensity analysis in bipolar subjects. arXiv preprint arXiv:1606.02231; De Choudhury, M., Gamon, M., Counts, S., Horvitz, E., Predicting depression via social media (2013) ICWSM, 2
- De Choudhury, M., Counts, S., Horvitz, E., (2013), Social media as a measurement tool of depression in populations. In: Proceedings of the 5th Annual ACM Web Science Conference, 47-56 (ACM); Esuli, A., Sebastiani, F., Sentiwordnet: a high-coverage lexical resource for opinion mining (2007) Evaluation, pp. 1-26
- Huys, Q.J., Moutoussis, M., Williams, J., Are computational models of any use to psychiatry? (2011) Neural Netw., 24, pp. 544-551
- John, G.H., Langley, P., (1995), Estimating continuous distributions in bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 338–345 (Morgan Kaufmann Publishers Inc.); Michel, J.-B., Quantitative analysis of culture using millions of digitized books (2011) Science, 331, pp. 176-182
- Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., Distributed representations of words and phrases and their compositionality (2013) Adv. Neural Inf. Process. Syst., pp. 3111-3119
- Mota, N., Carrillo, F., Slezak, D., Copelli, M., Ribeiro, S., (2016), Characterization of the relationship between semantic and structural language features in psychiatric diagnosis. In: Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 836-838 (IEEE); Mundt, J.C., Vogel, A.P., Feltner, D.E., Lenderking, W.R., Vocal acoustic biomarkers of depression severity and treatment response (2012) Biol. Psychiatry, 72, pp. 580-587
- Pestian, J.P., Matykiewicz, P., Grupp-Phelan, J., (2008), Using natural language processing to classify suicide notes. In: Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing, 96-97 (Association for Computational Linguistics); Wang, X.-J., Krystal, J.H., Computational psychiatry (2014) Neuron, 84, pp. 638-654
- Wiecki, T.V., Poland, J., Frank, M.J., Model-based cognitive neuroscience approaches to computational psychiatry clustering and classification (2015) Clin. Psychol. Sci., 3, pp. 378-399
- Williams, J., Scott, J., Autobiographical memory in depression (1988) Psychol. Med., 18, pp. 689-695
Citas:
---------- APA ----------
Carrillo, F., Sigman, M., Fernández Slezak, D., Ashton, P., Fitzgerald, L., Stroud, J., Nutt, D.J.,..., Carhart-Harris, R.L. (2018) . Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression. Journal of Affective Disorders, 230, 84-86.http://dx.doi.org/10.1016/j.jad.2018.01.006
---------- CHICAGO ----------
Carrillo, F., Sigman, M., Fernández Slezak, D., Ashton, P., Fitzgerald, L., Stroud, J., et al. "Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression" . Journal of Affective Disorders 230 (2018) : 84-86.http://dx.doi.org/10.1016/j.jad.2018.01.006
---------- MLA ----------
Carrillo, F., Sigman, M., Fernández Slezak, D., Ashton, P., Fitzgerald, L., Stroud, J., et al. "Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression" . Journal of Affective Disorders, vol. 230, 2018, pp. 84-86.http://dx.doi.org/10.1016/j.jad.2018.01.006
---------- VANCOUVER ----------
Carrillo, F., Sigman, M., Fernández Slezak, D., Ashton, P., Fitzgerald, L., Stroud, J., et al. Natural speech algorithm applied to baseline interview data can predict which patients will respond to psilocybin for treatment-resistant depression. J. Affective Disord. 2018;230:84-86.http://dx.doi.org/10.1016/j.jad.2018.01.006
