Citation

Corcoran CM, Cecchi GA. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2020; 5(8): 770-779.

Copyright

(Copyright © 2020, Society of Biological Psychiatry, Publisher Elsevier Publishing)

DOI

10.1016/j.bpsc.2020.06.004

PMID

32771179

PMCID

PMC7430500

Abstract

Increasingly, data-driven methods have been implemented to understand 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 examination. 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 suicide risk and detecting intoxication. Challenges and future directions are discussed, including biomarker development, harmonization, and 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. Finally, clinical neuroscience can inform the development of artificial intelligence.

Language: en

Keywords

Artificial Intelligence; Humans; Language; Natural Language Processing; Psychotic Disorders; Schizophrenia; Semantics; Speech; Speech graphs; Suicidal; Syntax