ArticleJournal of the American Medical Informatics Association : JAMIA2022
Detection of self-harm and suicidal ideation in emergency department triage notes.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Artificial intelligence-based risk assessment tools for sexual, reproductive and mental health: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Temporal and cross-site validation of an AI system for self-harm detection.PLOS digital health · 2026Article
- Evidence, use cases, and implementation safeguards of large language models in primary care.Communications medicine · 2026Review
- Patterns in health service use during and up to 1 year after an acute episode of hospital-presenting self-harm: data linkage cohort study of over 1.3 million records.BJPsych open · 2026Article
- Detecting Uncoded Self-Harm in Veterans' Electronic Health Records Using Positive and Unlabeled Learning: Retrospective Cohort Study.Journal of medical Internet research · 2026Article
- Youth visiting the emergency department after a suicide attempt, suicidal ideation or non-suicidal self-injury: trends, repeat visits and costs.BJPsych open · 2026Article
- Interventions for self-harm and suicidality in paediatric emergency departments: a meta-review.European child & adolescent psychiatry · 2025Review
- Suicide risk assessment and management needs of pre-examination and triage nurses in emergency departments: A qualitative study.Pakistan journal of medical sciences · 2025Article
- Automated detection and prediction of suicidal behavior from clinical notes using deep learning.PloS one · 2025Article
- Article
- Machine Learning-Based Prediction of Stroke in Emergency Departments.Therapeutic advances in neurological disorders · 2024Article
- Using machine learning to mine mental health diagnostic groups from emergency department presentations before and during the COVID-19 pandemic.Discover mental health · 2023Article
- Deep-learning-based natural-language-processing models to identify cardiovascular disease hospitalisations of patients with diabetes from routine visits' text.Scientific reports · 2023Article
- Daring discourse: artificial intelligence in pain medicine, opportunities and challenges.Regional anesthesia and pain medicine · 2023Article
- Identifying suicide documentation in clinical notes through zero-shot learning.Health science reports · 2023Article
- The Use and Structure of Emergency Nurses' Triage Narrative Data: Scoping Review.JMIR nursing · 2023Article
- Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing.Yearbook of medical informatics · 2022Article
- Addressing Consequential Public Health Problems Through Informatics and Data Science.Journal of the American Medical Informatics Association : JAMIA · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAccurate identification of self-harm presentations to Emergency Departments (ED) can lead to more timely mental health support, aid in understanding the burden of suicidal intent in a population, and support impact evaluation of public health initiatives related to suicide prevention. Given lack of manual self-harm reporting in ED, we aim to develop an automated system for the detection of self-harm presentations directly from ED triage notes. MATERIALS AND
methodsWe frame this as supervised classification using natural language processing (NLP), utilizing a large data set of 477 627 free-text triage notes from ED presentations in 2012-2018 to The Royal Melbourne Hospital, Australia. The data were highly imbalanced, with only 1.4% of triage notes relating to self-harm. We explored various preprocessing techniques, including spelling correction, negation detection, bigram replacement, and clinical concept recognition, and several machine learning methods.
resultsOur results show that machine learning methods dramatically outperform keyword-based methods. We achieved the best results with a calibrated Gradient Boosting model, showing 90% Precision and 90% Recall (PR-AUC 0.87) on blind test data. Prospective validation of the model achieves similar results (88% Precision; 89% Recall). DISCUSSION: ED notes are noisy texts, and simple token-based models work best. Negation detection and concept recognition did not change the results while bigram replacement significantly impaired model performance.
conclusionThis first NLP-based classifier for self-harm in ED notes has practical value for identifying patients who would benefit from mental health follow-up in ED, and for supporting surveillance of self-harm and suicide prevention efforts in the population.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.