ArticleScientific reports2018
Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 6 of them syntheses that pooled it.
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Who cites it
82 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Machine learning algorithms and their predictive accuracy for suicide and self-harm: Systematic review and meta-analysis.PLoS medicine · 2025Pooled it
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature.Frontiers in digital health · 2021Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Detection of Self-Harm in Electronic Mental Health Records Using Privacy-Preserving Local Language Models: Methodological Study.JMIR mental health · 2026Article
- Suicide Risk in 8- to 12-Year-Old Children: Retrospective Review of Emergency Department Electronic Health Records.JAACAP open · 2026Article
- Developing a Suicide Risk Prediction Algorithm Using Electronic Health Record Data in Mental Health Care: Real-World Case Study.JMIR medical informatics · 2026Article
- SPEAK-SAFE: secure processing of electronic audio for knowledge in suicide assessment from therapeutic exchanges.Frontiers in digital health · 2026Article
- Defining suicidality phenotypes for genetic studies: perspectives of the Psychiatric Genomics Consortium Suicide Working Group.Molecular psychiatry · 2025Review
- Developing a Tool for Identifying Clinical Risk From Free-Text Clinical Records: Natural Language Processing Study.JMIR AI · 2025Article
- Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm and Health Care Data.JMIR research protocols · 2025Article
- Article
- Improving Suicidal Ideation Detection in Social Media Posts: Topic Modeling and Synthetic Data Augmentation Approach.JMIR formative research · 2025Article
- Article
- Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach.Journal of medical Internet research · 2025Article
- Considerations and Challenges When Using Clinical and Vital Record Review for Suicide Research.Journal of patient safety · 2025Article
- Post-discharge suicide prediction among US veterans using natural language processing-enriched social and behavioral determinants of health.Npj mental health research · 2025Article
- The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.International journal of mental health nursing · 2025Review
22 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language Processing (NLP) strategies have been used with Electronic Health Records to increase information extraction from free text notes as well as structured fields concerning suicidality and this allows access to much larger cohorts than previously possible. This paper presents two novel NLP approaches - a rule-based approach to classify the presence of suicide ideation and a hybrid machine learning and rule-based approach to identify suicide attempts in a psychiatric clinical database. Good performance of the two classifiers in the evaluation study suggest they can be used to accurately detect mentions of suicide ideation and attempt within free-text documents in this psychiatric database. The novelty of the two approaches lies in the malleability of each classifier if a need to refine performance, or meet alternate classification requirements arises. The algorithms can also be adapted to fit infrastructures of other clinical datasets given sufficient clinical recording practice knowledge, without dependency on medical codes or additional data extraction of known risk factors to predict suicidal behaviour.
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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.