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Corcoran, C.M.; Carrillo, F.; Fernández-Slezak, D.; Bedi, G.; Klim, C.; Javitt, D.C.; Bearden, C.E.; Cecchi, G.A. "Prediction of psychosis across protocols and risk cohorts using automated language analysis" (2018) World Psychiatry. 17(1):67-75
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Abstract:

Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e.g., ultra) high-risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross-validate these automated linguistic analytic methods in a second larger risk cohort, also English-speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine-learning speech classifier – comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns – that had an 83% accuracy in predicting psychosis onset (intra-protocol), a cross-validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross-protocol), and a 72% accuracy in discriminating the speech of recent-onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at-risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry. © 2018 World Psychiatric Association

Registro:

Documento: Artículo
Título:Prediction of psychosis across protocols and risk cohorts using automated language analysis
Autor:Corcoran, C.M.; Carrillo, F.; Fernández-Slezak, D.; Bedi, G.; Klim, C.; Javitt, D.C.; Bearden, C.E.; Cecchi, G.A.
Filiación:Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
New York State Psychiatric Institute, New York, NY, United States
Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Instituto de Investigación en Ciencias de la Computación, Universidad de Buenos Aires, Buenos Aires, Argentina
Department of Psychiatry, Columbia University Medical Center, New York, NY, United States
Centre for Youth Mental Health, University of Melbourne, Orygen National Centre of Excellence in Youth Mental Health, Melbourne, Australia
Department of Psychiatry and Biobehavioral Sciences and Psychology, University of California Los Angeles, Semel Institute for Neuroscience and Human Behavior, Los Angeles, CA, United States
Computational Biology Center - Neuroscience, IBM T.J. Watson Research Center, Ossining, NY, United States
Palabras clave:Automated language analysis; high-risk youths; machine learning; prediction of psychosis; semantic coherence; syntactic complexity; adolescent; adult; area under the curve; Article; automated language analysis; classifier; clinical outcome; cohort analysis; controlled study; female; grammar; high risk population; human; language; machine learning; major clinical study; male; measurement accuracy; prediction; priority journal; psychosis; receiver operating characteristic; semantics; speech; speech analysis; validation study; validity; young adult
Año:2018
Volumen:17
Número:1
Página de inicio:67
Página de fin:75
DOI: http://dx.doi.org/10.1002/wps.20491
Título revista:World Psychiatry
Título revista abreviado:World Psychiatry
ISSN:17238617
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_17238617_v17_n1_p67_Corcoran

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Citas:

---------- APA ----------
Corcoran, C.M., Carrillo, F., Fernández-Slezak, D., Bedi, G., Klim, C., Javitt, D.C., Bearden, C.E.,..., Cecchi, G.A. (2018) . Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry, 17(1), 67-75.
http://dx.doi.org/10.1002/wps.20491
---------- CHICAGO ----------
Corcoran, C.M., Carrillo, F., Fernández-Slezak, D., Bedi, G., Klim, C., Javitt, D.C., et al. "Prediction of psychosis across protocols and risk cohorts using automated language analysis" . World Psychiatry 17, no. 1 (2018) : 67-75.
http://dx.doi.org/10.1002/wps.20491
---------- MLA ----------
Corcoran, C.M., Carrillo, F., Fernández-Slezak, D., Bedi, G., Klim, C., Javitt, D.C., et al. "Prediction of psychosis across protocols and risk cohorts using automated language analysis" . World Psychiatry, vol. 17, no. 1, 2018, pp. 67-75.
http://dx.doi.org/10.1002/wps.20491
---------- VANCOUVER ----------
Corcoran, C.M., Carrillo, F., Fernández-Slezak, D., Bedi, G., Klim, C., Javitt, D.C., et al. Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry. 2018;17(1):67-75.
http://dx.doi.org/10.1002/wps.20491