Prediction of psychosis across protocols and risk cohorts using automated language analysis

• Autores: Corcoran, Cheryl M.; Carrillo, Facundo; Fernandez Slezak, Diego; Bedi, Gillinder; Klim, Casimir; Javitt, Daniel C.; Bearden, Carrie E.; Cecchi, Guillermo Alberto
• Año de publicación: 2018
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: AUTOMATED LANGUAGE ANALYSIS; HIGH-RISK YOUTHS; MACHINE LEARNING; PREDICTION OF PSYCHOSIS; SEMANTIC COHERENCE; SYNTACTIC COMPLEXITY
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Corcoran, Cheryl M.. Icahn School of Medicine at Mount Sinai; Estados Unidos. New York State Psychiatric Institute; Estados Unidos
Fil: Carrillo, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Fernandez Slezak, Diego. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Bedi, Gillinder. New York State Psychiatric Institute; Estados Unidos. Columbia University; Estados Unidos. University of Melbourne; Australia
Fil: Klim, Casimir. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos
Fil: Javitt, Daniel C.. Columbia University; Estados Unidos. New York State Psychiatric Institute; Estados Unidos
Fil: Bearden, Carrie E.. University of California at Los Angeles; Estados Unidos
Fil: Cecchi, Guillermo Alberto. IBM T.J. Watson Research Center; Estados Unidos