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dc.contributor.authorElvevåg, Brita
dc.contributor.authorCohen, Alex S.
dc.date.accessioned2022-12-02T12:47:23Z
dc.date.available2022-12-02T12:47:23Z
dc.date.issued2022-09-01
dc.description.abstractNatural language processing (NLP) is a multidisciplinary field that involves objectifying aspects of language. Specifically, it understands human language by leveraging statistical and linguistic knowledge. NLP’s potential for enhancing the administration, accuracy, and objectivity of clinical assessments in psychiatry has been touted, as well as its potential for promoting equity in health care. This can be achieved through large-scale administration/automation, which in turn can improve the quality and frequency of services, and better connect people to their support teams; particularly those from underserved and marginalized communities. However, implementing NLP for clinical assessment is a complex endeavor that requires robust systems for ensuring reliability, validity, transparency, human oversight, and legal regulation of the resulting algorithmic and technological solutions. Adoption of any technology has both intended and unintended consequences, and this will probably be the case when leveraging NLP technology within schizophrenia assessment. The excitement around NLP’s potential in assessment in schizophrenia research has an almost frenzied feel to it. This can be seen in the steady increase in scientific articles and editorials1–4 and healthcare applications (eg,5–7). This seems like a good moment for calm reflection to consider the need for explicit research frameworks and trustworthy roadmaps for the journey ahead for both research purposes and for the eventual implementation of NLP-based tools in clinical practice. This themed issue of Schizophrenia Bulletin intends to provide such a moment of thoughtful reflection; and in doing so, contribute to a pathway for implementation in mainstream schizophrenia assessment. To do so we consider what realistically we should be expecting from machines and how we can meet this goal.en_US
dc.identifier.citationElvevåg, Cohen. Translating Natural Language Processing into Mainstream Schizophrenia Assessment. Schizophrenia Bulletin. 2022;48(5):936-938en_US
dc.identifier.cristinIDFRIDAID 2059145
dc.identifier.doi10.1093/schbul/sbac087
dc.identifier.issn0586-7614
dc.identifier.issn1745-1701
dc.identifier.urihttps://hdl.handle.net/10037/27675
dc.language.isoengen_US
dc.publisherOxford University Pressen_US
dc.relation.journalSchizophrenia Bulletin
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0en_US
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)en_US
dc.titleTranslating Natural Language Processing into Mainstream Schizophrenia Assessmenten_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)