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Pregled bibliografske jedinice broj: 1267218

Language as a biomarker for psychosis: A natural language processing approach


Corcoran, Cheryl M.; Mittal, Vijay A.; Bearden, Carrie E.; E. Gur, Raquel; Hitczenko, Kasia; Bilgrami, Zarina; Savic, Aleksandar; Cecchi, Guillermo A.; Wolff, Phillip
Language as a biomarker for psychosis: A natural language processing approach // Schizophrenia Research, 226 (2020), 158-166 doi:10.1016/j.schres.2020.04.032 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1267218 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Language as a biomarker for psychosis: A natural language processing approach

Autori
Corcoran, Cheryl M. ; Mittal, Vijay A. ; Bearden, Carrie E. ; E. Gur, Raquel ; Hitczenko, Kasia ; Bilgrami, Zarina ; Savic, Aleksandar ; Cecchi, Guillermo A. ; Wolff, Phillip

Izvornik
Schizophrenia Research (0920-9964) 226 (2020); 158-166

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Automated language analysis ; Clinical high risk ; Digital phenotyping ; Discourse coherence ; Latent semantic analysis ; Machine learning ; Natural language processing ; Psychosis ; Psychosis risk ; Referential coherence ; Schizophrenia ; Semantic coherence ; Semantic density ; Ultra high risk

Sažetak
Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies allow for the rapid and objective measurement of language features, many of which are not easily recognized by human raters. Here we review the key findings on language production disturbance in psychosis. We also describe recent advances in the computational methods used to analyze language data, including methods for the automatic measurement of discourse coherence, syntactic complexity, poverty of content, referential coherence, and metaphorical language. Linguistic biomarkers of psychosis risk are now undergoing cross-validation, with attention to harmonization of methods. Future directions in extended CHR networks include studies of sources of variance, and combination with other promising biomarkers of psychosis risk, such as cognitive and sensory processing impairments likely to be related to language. Implications for the broader study of social communication, including reciprocal prosody, face expression and gesture, are discussed.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti, Kognitivna znanost (prirodne, tehničke, biomedicina i zdravstvo, društvene i humanističke znanosti)



POVEZANOST RADA


Ustanove:
Medicinski fakultet, Zagreb,
Klinika za psihijatriju Vrapče

Profili:

Avatar Url Aleksandar Savić (autor)

Poveznice na cjeloviti tekst rada:

doi www.ncbi.nlm.nih.gov

Citiraj ovu publikaciju:

Corcoran, Cheryl M.; Mittal, Vijay A.; Bearden, Carrie E.; E. Gur, Raquel; Hitczenko, Kasia; Bilgrami, Zarina; Savic, Aleksandar; Cecchi, Guillermo A.; Wolff, Phillip
Language as a biomarker for psychosis: A natural language processing approach // Schizophrenia Research, 226 (2020), 158-166 doi:10.1016/j.schres.2020.04.032 (međunarodna recenzija, članak, znanstveni)
Corcoran, C., Mittal, V., Bearden, C., E. Gur, R., Hitczenko, K., Bilgrami, Z., Savic, A., Cecchi, G. & Wolff, P. (2020) Language as a biomarker for psychosis: A natural language processing approach. Schizophrenia Research, 226, 158-166 doi:10.1016/j.schres.2020.04.032.
@article{article, author = {Corcoran, Cheryl M. and Mittal, Vijay A. and Bearden, Carrie E. and E. Gur, Raquel and Hitczenko, Kasia and Bilgrami, Zarina and Savic, Aleksandar and Cecchi, Guillermo A. and Wolff, Phillip}, year = {2020}, pages = {158-166}, DOI = {10.1016/j.schres.2020.04.032}, keywords = {Automated language analysis, Clinical high risk, Digital phenotyping, Discourse coherence, Latent semantic analysis, Machine learning, Natural language processing, Psychosis, Psychosis risk, Referential coherence, Schizophrenia, Semantic coherence, Semantic density, Ultra high risk}, journal = {Schizophrenia Research}, doi = {10.1016/j.schres.2020.04.032}, volume = {226}, issn = {0920-9964}, title = {Language as a biomarker for psychosis: A natural language processing approach}, keyword = {Automated language analysis, Clinical high risk, Digital phenotyping, Discourse coherence, Latent semantic analysis, Machine learning, Natural language processing, Psychosis, Psychosis risk, Referential coherence, Schizophrenia, Semantic coherence, Semantic density, Ultra high risk} }
@article{article, author = {Corcoran, Cheryl M. and Mittal, Vijay A. and Bearden, Carrie E. and E. Gur, Raquel and Hitczenko, Kasia and Bilgrami, Zarina and Savic, Aleksandar and Cecchi, Guillermo A. and Wolff, Phillip}, year = {2020}, pages = {158-166}, DOI = {10.1016/j.schres.2020.04.032}, keywords = {Automated language analysis, Clinical high risk, Digital phenotyping, Discourse coherence, Latent semantic analysis, Machine learning, Natural language processing, Psychosis, Psychosis risk, Referential coherence, Schizophrenia, Semantic coherence, Semantic density, Ultra high risk}, journal = {Schizophrenia Research}, doi = {10.1016/j.schres.2020.04.032}, volume = {226}, issn = {0920-9964}, title = {Language as a biomarker for psychosis: A natural language processing approach}, keyword = {Automated language analysis, Clinical high risk, Digital phenotyping, Discourse coherence, Latent semantic analysis, Machine learning, Natural language processing, Psychosis, Psychosis risk, Referential coherence, Schizophrenia, Semantic coherence, Semantic density, Ultra high risk} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


Citati:





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