Using machine learning-based Natural Language Processing to quantify emergency department presentations related to suicide or self-harm in the Australian Capital Territory.
Background: Suicide and self-harm are significant issues globally. Accurate, efficient and comprehensive data are required to identify people who present to Emergency Departments due to self-harm to receive current accepted interventions and to develop effective health policies and responses. Curren...
| Published in: | Australian & New Zealand Journal of Psychiatry Vol. 60; no. 5; pp. 443 - 454 |
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| Main Authors: | , , , , , , , , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Sage Publications Inc.
May2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193250225&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193250225 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00048674 7ZU jtl: Australian & New Zealand Journal of Psychiatry issn: 00048674 maglogo: Y pubinfo: dt: May2026 vid: 60 iid: 5 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 193250225 191961374 193250225 193250225 10.1177/00048674261418834 193250225 ppf: 443 ppct: 11 formats: tig: atl: Using machine learning-based Natural Language Processing to quantify emergency department presentations related to suicide or self-harm in the Australian Capital Territory. aug: au: McNamara, George Mayers, Paul Draper, Glenn Walsh, Erin I Zhu, Gao Moore, Elizabeth Raulli, Alexandra Chalker, Elizabeth Nicol, Marcus Freebairn, Louise affil: Epidemiology Section, ACT Health Directorate, Canberra, ACT, Australia sug: subj: Machine Learning Utilization Natural Language Processing Utilization Emergency Service Statistics and Numerical Data Suicide Injuries, Self-Inflicted Australia Self-Injurious Behavior Evaluation Program Evaluation Human Australia Funding Source Multimethod Studies Triage Documentation Descriptive Statistics Data Mining Health Information Systems Mental Health Services ab: Background: Suicide and self-harm are significant issues globally. Accurate, efficient and comprehensive data are required to identify people who present to Emergency Departments due to self-harm to receive current accepted interventions and to develop effective health policies and responses. Current methods for identifying people presenting with these behaviors can be time- and labor-intensive or can underestimate the true figure. Methods: This study investigated the use of a novel machine learning-based Natural Language Processing program developed to quantify the number of Emergency Department presentations which were related to suicidal or self-harm ideation or behavior. The program identifies these presentations based on Emergency Department triage notes. We compared the Natural Language Processing program with alternative methods for identifying suicide or self-harm related presentations, including International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification coding and keyword searching. Results: Using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification codes included with the dataset, 10,399 Emergency Department presentations related to suicide or self-harm were identified for the period July 2015 to June 2022, while the Natural Language Processing program found 27,298 presentations over the same period with a precision of 0.89 and a recall of 0.94. All methods were evaluated by comparing their identifications with a set of manually identified presentations. Natural Language Processing identification was the most appropriate for providing an accurate, comprehensive and efficient quantification. Conclusion: This study revealed that less than 40% of Emergency Department presentations related to suicide or self-harm are identified using existing methods in the Australian Capital Territory. By providing an improved identification method, this study enables more accurate analysis and understanding of the issues of suicide and self-harm. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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