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

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 disturba...

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Detalles Bibliográficos
Autores principales: Corcoran, C.M., Carrillo, F., Fernández-Slezak, D., Bedi, G., Klim, C., Javitt, D.C., Bearden, C.E., Cecchi, G.A.
Formato: JOUR
Materias:
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
major clinical study
male
measurement accuracy
prediction
priority journal
psychosis
receiver operating characteristic
semantics
speech
speech analysis
validation study
validity
young adult
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_17238617_v17_n1_p67_Corcoran
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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