Automated analysis of free speech predicts psychosis onset in high-risk youths
- Autores
- Bedi, Gillinder; Carrillo, Facundo; Cecchi, Guillermo Alberto; Fernandez Slezak, Diego; Sigman, Mariano; Mota, Natália; Ribeiro, Sidarta; Javitt, Daniel; Copelli, Mauro; Corcoran, Cheryl
- Año de publicación
- 2015
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- 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
Fil: Bedi, Gillinder. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos
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
Fil: Cecchi, Guillermo Alberto. Ibm Research. Thomas J. Watson Research Center; Estados Unidos
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
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
Fil: Mota, Natália. Universidade Federal do Rio Grande do Norte; Brasil
Fil: Ribeiro, Sidarta. Universidade Federal do Rio Grande do Norte; Brasil
Fil: Javitt, Daniel. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos
Fil: Copelli, Mauro. Universidade Federal de Pernambuco; Brasil
Fil: Corcoran, Cheryl. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos - Materia
-
SCHIZOPHRENIA
NEUROSCIENCE - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/44639
Ver los metadatos del registro completo
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Automated analysis of free speech predicts psychosis onset in high-risk youthsBedi, GillinderCarrillo, FacundoCecchi, Guillermo AlbertoFernandez Slezak, DiegoSigman, MarianoMota, NatáliaRibeiro, SidartaJavitt, DanielCopelli, MauroCorcoran, CherylSCHIZOPHRENIANEUROSCIENCEhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1BACKGROUND/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 2015Fil: Bedi, Gillinder. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados UnidosFil: Carrillo, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Cecchi, Guillermo Alberto. Ibm Research. Thomas J. Watson Research Center; Estados UnidosFil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Mota, Natália. Universidade Federal do Rio Grande do Norte; BrasilFil: Ribeiro, Sidarta. Universidade Federal do Rio Grande do Norte; BrasilFil: Javitt, Daniel. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados UnidosFil: Copelli, Mauro. Universidade Federal de Pernambuco; BrasilFil: Corcoran, Cheryl. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados UnidosNature Publishing Group2015-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/44639Bedi, 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; 8-20152334-265XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1038/npjschz.2015.30info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/npjschz201530info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:59:56Zoai:ri.conicet.gov.ar:11336/44639instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:59:56.96CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Automated analysis of free speech predicts psychosis onset in high-risk youths |
title |
Automated analysis of free speech predicts psychosis onset in high-risk youths |
spellingShingle |
Automated analysis of free speech predicts psychosis onset in high-risk youths Bedi, Gillinder SCHIZOPHRENIA NEUROSCIENCE |
title_short |
Automated analysis of free speech predicts psychosis onset in high-risk youths |
title_full |
Automated analysis of free speech predicts psychosis onset in high-risk youths |
title_fullStr |
Automated analysis of free speech predicts psychosis onset in high-risk youths |
title_full_unstemmed |
Automated analysis of free speech predicts psychosis onset in high-risk youths |
title_sort |
Automated analysis of free speech predicts psychosis onset in high-risk youths |
dc.creator.none.fl_str_mv |
Bedi, Gillinder Carrillo, Facundo Cecchi, Guillermo Alberto Fernandez Slezak, Diego Sigman, Mariano Mota, Natália Ribeiro, Sidarta Javitt, Daniel Copelli, Mauro Corcoran, Cheryl |
author |
Bedi, Gillinder |
author_facet |
Bedi, Gillinder Carrillo, Facundo Cecchi, Guillermo Alberto Fernandez Slezak, Diego Sigman, Mariano Mota, Natália Ribeiro, Sidarta Javitt, Daniel Copelli, Mauro Corcoran, Cheryl |
author_role |
author |
author2 |
Carrillo, Facundo Cecchi, Guillermo Alberto Fernandez Slezak, Diego Sigman, Mariano Mota, Natália Ribeiro, Sidarta Javitt, Daniel Copelli, Mauro Corcoran, Cheryl |
author2_role |
author author author author author author author author author |
dc.subject.none.fl_str_mv |
SCHIZOPHRENIA NEUROSCIENCE |
topic |
SCHIZOPHRENIA NEUROSCIENCE |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
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 Fil: Bedi, Gillinder. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos 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 Fil: Cecchi, Guillermo Alberto. Ibm Research. Thomas J. Watson Research Center; Estados Unidos 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 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 Fil: Mota, Natália. Universidade Federal do Rio Grande do Norte; Brasil Fil: Ribeiro, Sidarta. Universidade Federal do Rio Grande do Norte; Brasil Fil: Javitt, Daniel. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos Fil: Copelli, Mauro. Universidade Federal de Pernambuco; Brasil Fil: Corcoran, Cheryl. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos |
description |
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 |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-08 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/44639 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 2334-265X CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/44639 |
identifier_str_mv |
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 2334-265X CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/altIdentifier/doi/10.1038/npjschz.2015.30 info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/npjschz201530 |
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openAccess |
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application/pdf application/pdf application/pdf application/pdf |
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Nature Publishing Group |
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Nature Publishing Group |
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