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    Automatic mining of symptom severity from psychiatric evaluation notes.

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    Karystianis_et_al-2017-Interna ...
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    Authors
    Karystianis, G
    Nevado, A
    Kim, C
    Dehghan, Azad
    Keane, J
    Nenadic, G
    Affiliation
    Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
    Issue Date
    2017-12-22
    
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    Abstract
    As 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.
    Citation
    Automatic mining of symptom severity from psychiatric evaluation notes. 2017 Int J Methods Psychiatr Res
    Journal
    International Journal of Methods in Psychiatric Research
    URI
    http://hdl.handle.net/10541/620788
    DOI
    10.1002/mpr.1602
    PubMed ID
    29271009
    Type
    Article
    Language
    en
    ISSN
    1557-0657
    ae974a485f413a2113503eed53cd6c53
    10.1002/mpr.1602
    Scopus Count
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