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Computational phenotyping in psychosis risk: Language, speech acoustics and face expression

Abstract Description

Increasingly, data-driven methods have been implemented to understand the structure and causes of psychopathology. Language is the main source of information in psychiatry and represents “big data” at the level of the individual. Language and behavior are amenable to computational “natural language processing” (NLP) analytics, which may help operationalize the mental status exam. In this review, we highlight the application of NLP to schizophrenia and its risk states as an exemplar of its use, operationalizing tangential and concrete speech as reductions in semantic coherence and syntactic complexity, respectively. Other clinical applications are reviewed, including forecasting of suicide risk and detection of intoxication. Future directions include the application of NLP more broadly to behavior, including intonation/prosody, facial expression and gesture, and the integration of these in dyads and during discourse. Similar NLP analytics can also be applied beyond humans to behavioral motifs across species, important for modeling psychopathology in animal models.