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dc.contributor.author Bedi, Gillinder
dc.contributor.author Carrillo, Facundo
dc.contributor.author Cecchi, Guillermo Alberto
dc.contributor.author Fernandez Slezak, Diego
dc.contributor.author Sigman, Mariano
dc.contributor.author Mota, Natália
dc.contributor.author Ribeiro, Sidarta
dc.contributor.author Javitt, Daniel
dc.contributor.author Copelli, Mauro
dc.contributor.author Corcoran, Cheryl
dc.date.available 2018-05-09T17:47:53Z
dc.date.issued 2015-08
dc.identifier.citation Bedi, Gillinder; Carrillo, Facundo; Cecchi, Guillermo Alberto; Fernandez Slezak, Diego; Sigman, Mariano; et al.; Automated analysis of free speech predicts psychosis onset in high-risk youths; Nature Publishing Group; npj Schizophrenia; 1; 8-2015
dc.identifier.issn 2334-265X
dc.identifier.uri http://hdl.handle.net/11336/44639
dc.description.abstract BACKGROUND/OBJECTIVES: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novelcomputerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illnessin individuals.AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predictlater psychosis onset in youths at clinical high-risk (CHR) for psychosis.METHODS: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; fivetransitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic featurespredicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-outcross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features andprodromal symptom ratings was computed.RESULTS: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markersof speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosisdevelopment with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantlycorrelated with prodromal symptoms.CONCLUSIONS: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental statechanges in emergent psychosis. Recent developments in computer science, including natural language processing, could providethe foundation for future development of objective clinical tests for psychiatry.npj Schizophrenia (2015) 1, Article number: 15030; doi:10.1038/npjschz.2015.30; published online 26 August 2015
dc.format application/pdf
dc.language.iso eng
dc.publisher Nature Publishing Group
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject SCHIZOPHRENIA
dc.subject NEUROSCIENCE
dc.subject.classification Ciencias de la Computación
dc.subject.classification Ciencias de la Computación e Información
dc.subject.classification CIENCIAS NATURALES Y EXACTAS
dc.title Automated analysis of free speech predicts psychosis onset in high-risk youths
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2018-05-04T21:32:26Z
dc.journal.volume 1
dc.journal.pais Estados Unidos
dc.description.fil Fil: Bedi, Gillinder. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos
dc.description.fil Fil: Carrillo, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina
dc.description.fil Fil: Cecchi, Guillermo Alberto. Ibm Research. Thomas J. Watson Research Center; Estados Unidos
dc.description.fil Fil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina
dc.description.fil Fil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
dc.description.fil Fil: Mota, Natália. Universidade Federal do Rio Grande do Norte; Brasil
dc.description.fil Fil: Ribeiro, Sidarta. Universidade Federal do Rio Grande do Norte; Brasil
dc.description.fil Fil: Javitt, Daniel. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos
dc.description.fil Fil: Copelli, Mauro. Universidade Federal de Pernambuco; Brasil
dc.description.fil Fil: Corcoran, Cheryl. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos
dc.journal.title npj Schizophrenia
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1038/npjschz.2015.30
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/npjschz201530
dc.conicet.fuente unificacion


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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution 2.5 Unported (CC BY 2.5)