ArticleInternational journal of methods in psychiatric research2017
Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study.
Article in International journal of methods in psychiatric research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 4 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis.PLoS medicine · 2025Pooled it
- Structured data vs. unstructured data in machine learning prediction models for suicidal behaviors: A systematic review and meta-analysis.Frontiers in digital health · 2022Pooled it
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Detecting Pediatric Emergency Service Use for Suicide and Self-Harm: Multimodal Analysis of 3828 Encounters.JMIR mental health · 2026Article
- MONDEP: A unified SpatioTemporal MONitoring Framework for National DEPression Forecasting.Heliyon · 2024Article
- Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality.Npj mental health research · 2024Article
- Article
- Predicting suicide attempts among Norwegian adolescents without using suicide-related items: a machine learning approach.Frontiers in psychiatry · 2023Article
- Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.Frontiers in psychiatry · 2023Article
- ScAN: Suicide Attempt and Ideation Events Dataset.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2022Article
- Autism spectrum disorders as a risk factor for adolescent self-harm: a retrospective cohort study of 113,286 young people in the UK.BMC medicine · 2022Article
- Development of a Self-Harm Monitoring System for Victoria.International journal of environmental research and public health · 2020Article
- Identifying and Predicting Intentional Self-Harm in Electronic Health Record Clinical Notes: Deep Learning Approach.JMIR medical informatics · 2020Article
- Selection of Clinical Text Features for Classifying Suicide Attempts.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark.JAMA psychiatry · 2020Article
- Article
- Reviewing a Decade of Research Into Suicide and Related Behaviour Using the South London and Maudsley NHS Foundation Trust Clinical Record Interactive Search (CRIS) System.Frontiers in psychiatry · 2020Review
- Identifying Emergency Department Symptom-Based Diagnoses with the Unified Medical Language System.The western journal of emergency medicine · 2019Article
- Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior.Frontiers in psychiatry · 2019Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to determine whether an expert system based on automated processing of electronic health records (EHRs) could provide a more accurate estimate of the annual rate of emergency department (ED) visits for suicide attempts in France, as compared to the current national surveillance system based on manual coding by emergency practitioners. A feasibility study was conducted at Lyon University Hospital, using data for all ED patient visits in 2012. After automatic data extraction and pre-processing, including automatic coding of medical free-text through use of the Unified Medical Language System, seven different machine-learning methods were used to classify the reasons for ED visits into "suicide attempts" versus "other reasons". The performance of these different methods was compared by using the F-measure. In a test sample of 444 patients admitted to the ED in 2012 (98 suicide attempts, 48 cases of suicidal ideation, and 292 controls with no recorded non-fatal suicidal behaviour), the F-measure for automatic detection of suicide attempts ranged from 70.4% to 95.3%. The random forest and naïve Bayes methods performed best. This study demonstrates that machine-learning methods can improve the quality of epidemiological indicators as compared to current national surveillance of suicide attempts.
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Registered trials
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.