Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop. (22nd March 2023)

• Record Type: Journal Article
• Authors: Corona Hernández, Hugo; Corcoran, Cheryl; Achim, Amélie M; de Boer, Janna N; Boerma, Tessel; Brederoo, Sanne G; Cecchi, Guillermo A; Ciampelli, Silvia; Elvevåg, Brita; Fusaroli, Riccardo; Giordano, Silvia; Hauglid, Mathias; van Hessen, Arjan; Hinzen, Wolfram; Homan, Philipp; de Kloet, Sybren F; Koops, Sanne; Kuperberg, Gina R; Maheshwari, Kritika; Mota, Natalia B; Parola, Alberto; Rocca, Roberta; Sommer, Iris E C; Truong, Khiet; Voppel, Alban E; van Vugt, Marieke; Wijnen, Frank; Palaniyappan, Lena
• Is Part Of: Schizophrenia bulletin. Volume 49(2023)Supplement 2
• Page Start: S86
• Page End: S92
• Publication Date: 2023-03-22
• Subjects: speech technology -- implementation -- digital markers -- psychiatric practice -- pathophysiology; Schizophrenia -- Periodicals; Schizophrenia -- Research -- Periodicals; 616.898005
• DOI: 10.1093/schbul/sbac215
• Languages: English
• ISSNs: 0586-7614

Abstract

Abstract: This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.