ReviewCurrent epidemiology reports2022
A Critical Review of Text Mining Applications for Suicide Research.
Review in Current epidemiology reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
15 citing papers in PubMed, 27 citations in OpenAlex.
- Comparing Suicide Rates Across Subpopulations of Veterans Receiving Veterans Health Administration Care, 2017-2021: The Suicide Risk Encyclopedia.Administration and policy in mental health · 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
- Detecting Pediatric Emergency Service Use for Suicide and Self-Harm: Multimodal Analysis of 3828 Encounters.JMIR mental health · 2026Article
- Developing a Suicide Risk Prediction Algorithm Using Electronic Health Record Data in Mental Health Care: Real-World Case Study.JMIR medical informatics · 2026Article
- A Pilot Qualitative Study to Better Understand the Factors Related to Suicides and Inform Public Health Action Across a Predominantly Coastal and Rural Area: Cornwall, Southwest of England.International journal of environmental research and public health · 2025Article
- Natural language processing to identify suicidal ideation and anhedonia in major depressive disorder.BMC medical informatics and decision making · 2025Article
- Automatically extracting social determinants of health for suicide: a narrative literature review.Npj mental health research · 2024Review
- Global Suicide Mortality Rates (2000-2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data.International journal of environmental research and public health · 2024Article
- Characteristics of and Variation in Suicide Mortality Related to Retirement During the Great Recession: Perspectives From the National Violent Death Reporting System.The Gerontologist · 2024Article
- Combining sentiment analysis and text mining with content analysis of farm vet interviews on mental wellbeing in livestock practice.PloS one · 2024Article
- CuentosIE: can a chatbot about "tales with a message" help to teach emotional intelligence?PeerJ. Computer science · 2024Article
- Examining Intimate Partner Violence-Related Fatalities: Past Lessons and Future Directions Using U.S. National Data.Journal of family violence · 2023Article
- Understanding Suicide over the Life Course Using Data Science Tools within a Triangulation Framework.Journal of psychiatry and brain science · 2023Article
- Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk.Frontiers in psychiatry · 2023Article
- Dynamic suicide topic modelling: Deriving population-specific, psychosocial and time-sensitive suicide risk variables from Electronic Health Record psychotherapy notes.Clinical psychology & psychotherapyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose of Review: Applying text mining to suicide research holds a great deal of promise. In this manuscript, literature from 2019 to 2021 is critically reviewed for text mining projects that use electronic health records, social media data, and death records. Recent Findings: Text mining has helped identify risk factors for suicide in general and specific populations (e.g., older adults), has been combined with structured variables in EHRs to predict suicide risk, and has been used to track trends in social media suicidal discourse following population level events (e.g., COVID-19, celebrity suicides). Summary: Future research should utilize text mining along with data linkage methods to capture more complete information on risk factors and outcomes across data sources (e.g., combining death records and EHRs), evaluate effectiveness of NLP-based intervention programs that use suicide risk prediction, establish standards for reporting accuracy of text mining programs to enable comparison across studies, and incorporate implementation science to understand feasibility, acceptability, and technical considerations.
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.