Using machine learning-based Natural Language Processing to quantify emergency department presentations related to suicide or self-harm in the Australian Capital Territory.

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Bibliographic Details
Title: Using machine learning-based Natural Language Processing to quantify emergency department presentations related to suicide or self-harm in the Australian Capital Territory.
Authors: McNamara G; Epidemiology Section, ACT Health Directorate, Canberra, ACT, Australia., Mayers P; ACT Office for Mental Health and Wellbeing, ACT Health Directorate, Canberra, ACT, Australia., Draper G; Epidemiology Section, ACT Health Directorate, Canberra, ACT, Australia., Walsh EI; National Centre for Epidemiology and Population Health (NCEPH), Australian National University, Canberra, ACT, Australia., Zhu G; Epidemiology Section, ACT Health Directorate, Canberra, ACT, Australia., Moore E; Royal Australian and New Zealand College of Psychiatrists, Melbourne, VIC, Australia., Raulli A; Epidemiology Section, ACT Health Directorate, Canberra, ACT, Australia., Chalker E; Epidemiology Section, ACT Health Directorate, Canberra, ACT, Australia.; National Centre for Epidemiology and Population Health (NCEPH), Australian National University, Canberra, ACT, Australia., Nicol M; Data Analytics Branch, ACT Health Directorate, Canberra, ACT, Australia., Freebairn L; Epidemiology Section, ACT Health Directorate, Canberra, ACT, Australia.; National Centre for Epidemiology and Population Health (NCEPH), Australian National University, Canberra, ACT, Australia.
Source: The Australian and New Zealand journal of psychiatry [Aust N Z J Psychiatry] 2026 May; Vol. 60 (5), pp. 443-453. Date of Electronic Publication: 2026 Feb 28.
Publication Type: Journal Article
Journal Info: Publisher: Sage Country of Publication: England NLM ID: 0111052 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1440-1614 (Electronic) Linking ISSN: 00048674 NLM ISO Abbreviation: Aust N Z J Psychiatry Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1440-1614
DOI:10.1177/00048674261418834