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dc.contributor.authorKarystianis, G
dc.contributor.authorNevado, A
dc.contributor.authorKim, C
dc.contributor.authorDehghan, Azad
dc.contributor.authorKeane, J
dc.contributor.authorNenadic, G
dc.date.accessioned2018-01-18T13:33:26Z
dc.date.available2018-01-18T13:33:26Z
dc.date.issued2017-12-22
dc.identifier.citationAutomatic mining of symptom severity from psychiatric evaluation notes. 2017 Int J Methods Psychiatr Resen
dc.identifier.issn1557-0657
dc.identifier.pmid29271009
dc.identifier.doi10.1002/mpr.1602
dc.identifier.urihttp://hdl.handle.net/10541/620788
dc.description.abstractAs electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free-text narrative aiming to support epidemiological research and clinical decision-making. In this paper, we explore extraction of explicit mentions of symptom severity from initial psychiatric evaluation records. We use the data provided by the 2016 CEGS N-GRID NLP shared task Track 2, which contains 541 records manually annotated for symptom severity according to the Research Domain Criteria.
dc.language.isoenen
dc.rightsArchived with thanks to International journal of methods in psychiatric researchen
dc.titleAutomatic mining of symptom severity from psychiatric evaluation notes.en
dc.typeArticleen
dc.contributor.departmentCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australiaen
dc.identifier.journalInternational Journal of Methods in Psychiatric Researchen
refterms.dateFOA2018-12-17T15:12:03Z
html.description.abstractAs electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free-text narrative aiming to support epidemiological research and clinical decision-making. In this paper, we explore extraction of explicit mentions of symptom severity from initial psychiatric evaluation records. We use the data provided by the 2016 CEGS N-GRID NLP shared task Track 2, which contains 541 records manually annotated for symptom severity according to the Research Domain Criteria.


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