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Text classification...
Text classification to inform suicide risk assessment in electronic health records
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Bittar, A. (författare)
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- Velupillai, Sumithra (författare)
- KTH,Teoretisk datalogi, TCS,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Roberts, A. (författare)
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Dutta, R. (författare)
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(creator_code:org_t)
- IOS Press, 2019
- 2019
- Engelska.
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Ingår i: 17th World Congress on Medical and Health Informatics, MEDINFO 2019. - : IOS Press. - 9781643680026 ; , s. 40-44
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- Assessing a patient's risk of an impending suicide attempt has been hampered by limited information about dynamic factors that change rapidly in the days leading up to an attempt. The storage of patient data in electronic health records (EHRs) has facilitated population-level risk assessment studies using machine learning techniques. Until recently, most such work has used only structured EHR data and excluded the unstructured text of clinical notes. In this article, we describe our experiments on suicide risk assessment, modelling the problem as a classification task. Given the wealth of text data in mental health EHRs, we aimed to assess the impact of using this data in distinguishing periods prior to a suicide attempt from those not preceding such an attempt. We compare three different feature sets, one structured and two text-based, and show that inclusion of text features significantly improves classification accuracy in suicide risk assessment.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- Natural Language Processing
- Risk Assessment
- Suicide
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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