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Text classification to inform suicide risk assessment in electronic health records
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS. Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.ORCID iD: 0000-0002-4178-2980
2019 (English)In: 17th World Congress on Medical and Health Informatics, MEDINFO 2019, IOS Press, 2019, Vol. 264, p. 40-44Conference paper, Published paper (Refereed)
Abstract [en]

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. © 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).

Place, publisher, year, edition, pages
IOS Press, 2019. Vol. 264, p. 40-44
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 264
Keywords [en]
Natural Language Processing, Risk Assessment, Suicide
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-262521DOI: 10.3233/SHTI190179PubMedID: 31437881Scopus ID: 2-s2.0-85071499215ISBN: 9781643680026 (print)OAI: oai:DiVA.org:kth-262521DiVA, id: diva2:1362304
Conference
17th World Congress on Medical and Health Informatics, MEDINFO 2019; Lyon; France; 25 August 2019 through 30 August 2019
Note

QC 20191018

Available from: 2019-10-18 Created: 2019-10-18 Last updated: 2020-03-09Bibliographically approved

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Velupillai, Sumithra

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