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Identifying suicida...
Identifying suicidal adolescents from mental health records using natural language processing
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- Velupillai, Sumithra (author)
- KTH,Teoretisk datalogi, TCS,King's College London, London, United Kingdom
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Epstein, S. (author)
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Bittar, A. (author)
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Stephenson, T. (author)
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Dutta, R. (author)
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Downs, J. (author)
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(creator_code:org_t)
- IOS Press, 2019
- 2019
- English.
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In: 17th World Congress on Medical and Health Informatics, MEDINFO 2019. - : IOS Press. - 9781643680026 ; , s. 413-417
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Subject headings
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- Suicidal ideation is a risk factor for self-harm, completed suicide and can be indicative of mental health issues. Adolescents are a particularly vulnerable group, but few studies have examined suicidal behaviour prevalence in large cohorts. Electronic Health Records (EHRs) are a rich source of secondary health care data that could be used to estimate prevalence. Most EHR documentation related to suicide risk is written in free text, thus requiring Natural Language Processing (NLP) approaches. We adapted and evaluated a simple lexicon- and rule-based NLP approach to identify suicidal adolescents from a large EHR database. We developed a comprehensive manually annotated EHR reference standard and assessed NLP performance at both document and patient level on data from 200 patients (~5000 documents). We achieved promising results (>80% f1 score at both document and patient level). Simple NLP approaches can be successfully used to identify patients who exhibit suicidal risk behaviour, and our proposed approach could be useful for other populations and settings.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Annan hälsovetenskap (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Other Health Sciences (hsv//eng)
Keyword
- Electronic Health Records
- Natural Language Processing
- Suicide
Publication and Content Type
- ref (subject category)
- kon (subject category)
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