PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 3, 2026Australian & New Zealand Journal of Psychiatry2 citations

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

View Full Paper
GMGeorge McNamaraPMPaul MayersGDGlenn Draper

Key Points

  • The aim is to enhance the identification of emergency department presentations related to suicide and self-harm.
  • Utilized machine learning and natural language processing for analysis
  • Focused on emergency department presentations in the Australian Capital Territory
  • Evaluated existing identification methods for effectiveness
  • Less than 40% of suicide or self-harm cases were identified using current methods
  • The new identification method significantly improves accuracy
  • Increased potential for better understanding and addressing suicide and self-harm issues

Abstract

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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

McNamara et al. (2026) studied this question.

synapsesocial.com/papers/69a67f12f353c071a6f0af2chttps://doi.org/10.1177/00048674261418834
Ask AI
Helpful
Bookmark
Share
View Full Paper