ArticlePloS one2014
Predicting the risk of suicide by analyzing the text of clinical notes.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 67 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed, 3 syntheses or guidelines pooled it, 173 citations in OpenAlex.
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Researching Mental Health Disorders in the Era of Social Media: Systematic Review.Journal of medical Internet research · 2017Pooled it
- Detection of acute 3,4-methylenedioxymethamphetamine (MDMA) effects across protocols using automated natural language processing.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2020Trial
- Article
- Predicting imminent suicide risk in a crisis hotline chat using machine learning.Scientific reports · 2025Article
- Impacts of cognitive forcing and need for cognition on biased AI-assisted decision making about mental health emergencies.Scientific reports · 2025Article
- Screening and Risk Algorithms for Detecting Pediatric Suicide Risk in the Emergency Department.JAMA network open · 2025Article
- Machine learning applications related to suicide in military and Veterans: A scoping literature review.Journal of biomedical informatics · 2025Article
- Considerations and Challenges When Using Clinical and Vital Record Review for Suicide Research.Journal of patient safety · 2025Article
- Using Natural Language Processing to Inform Targeted Rural and Urban Hispanic VA Suicide Prediction Models.Rural mental health · 2025Article
- Investigating the Differential Impact of Psychosocial Factors by Patient Characteristics and Demographics on Veteran Suicide Risk Through Machine Learning Extraction of Cross-Modal Interactions.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Automated detection and prediction of suicidal behavior from clinical notes using deep learning.PloS one · 2025Article
- Using natural language processing to evaluate temporal patterns in suicide risk variation among high-risk Veterans.Psychiatry research · 2024Article
- Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records.Translational psychiatry · 2024Article
- Predicting adolescent suicidal behavior following inpatient discharge using structured and unstructured data.Journal of affective disorders · 2024Article
- Explainable Suicide Phenotyping from Initial Psychiatric Evaluation Notes Using Reasoning Large Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Combining clinical notes with structured electronic health records enhances the prediction of mental health crises.Cell reports. Medicine · 2023Article
- Intentional Self-Harm Among US Veterans With Traumatic Brain Injury or Posttraumatic Stress Disorder: Retrospective Cohort Study From 2008 to 2017.JMIR public health and surveillance · 2023Article
- Leveraging Natural Language Processing to Improve Electronic Health Record Suicide Risk Prediction for Veterans Health Administration Users.The Journal of clinical psychiatry · 2023Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
We developed linguistics-driven prediction models to estimate the risk of suicide. These models were generated from unstructured clinical notes taken from a national sample of U.S. Veterans Administration (VA) medical records. We created three matched cohorts: veterans who committed suicide, veterans who used mental health services and did not commit suicide, and veterans who did not use mental health services and did not commit suicide during the observation period (n = 70 in each group). From the clinical notes, we generated datasets of single keywords and multi-word phrases, and constructed prediction models using a machine-learning algorithm based on a genetic programming framework. The resulting inference accuracy was consistently 65% or more. Our data therefore suggests that computerized text analytics can be applied to unstructured medical records to estimate the risk of suicide. The resulting system could allow clinicians to potentially screen seemingly healthy patients at the primary care level, and to continuously evaluate the suicide risk among psychiatric patients.
Indexed as
Identifiers
What OpenQuestion holds
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.