ArticleJournal of medical Internet research2018
Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study.
Article in Journal of medical Internet research, 2018. 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 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Artificial intelligence in intimate partner violence risk pathways: a PRISMA-ScR review of femicide prevention and medico-legal accountability.Frontiers in digital health · 2026Pooled it
- Understanding Verbal Violence Perpetration Among Intimate Partner Stalkers Using Police-Identified Psychological Distress and Drug Use.Criminal behaviour and mental health : CBMH · 2025Article
- Older Perpetrators of Domestic Violence: Mixed-Effects Logistic Regression Analysis of Police Records.JMIR aging · 2025Article
- Who you gonna call? Examining police reports involving people with psychosis to improve front line management.Frontiers in psychiatry · 2025Article
- Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives.Journal of quantitative criminology · 2025Article
- Characteristics of domestic violence perpetrators with dementia from police records using text mining.Frontiers in psychiatry · 2024Article
- Advancing psychosocial disability and psychosocial rehabilitation research through large language models and computational text mining.Global mental health (Cambridge, England) · 2024Article
- Mental Illness Concordance Between Hospital Clinical Records and Mentions in Domestic Violence Police Narratives: Data Linkage Study.JMIR formative research · 2022Article
- Surveillance of Domestic Violence Using Text Mining Outputs From Australian Police Records.Frontiers in psychiatry · 2021Article
- Listening to Mental Health Crisis Needs at Scale: Using Natural Language Processing to Understand and Evaluate a Mental Health Crisis Text Messaging Service.Frontiers in digital health · 2021Article
- Utilizing Text Mining, Data Linkage and Deep Learning in Police and Health Records to Predict Future Offenses in Family and Domestic Violence.Frontiers in digital health · 2021Article
- Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study.Journal of medical Internet research · 2020Article
- Development and Validation of a Machine Learning Individualized Treatment Rule in First-Episode Schizophrenia.JAMA network open · 2020Article
- Automated Analysis of Domestic Violence Police Reports to Explore Abuse Types and Victim Injuries: Text Mining Study.Journal of medical Internet research · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundVast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes.
objectiveIn this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text.
methodsWe used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims.
resultsThe precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.25%, 3269) and POIs (18.70%, 8944), followed by alcohol abuse for POIs (12.19%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.66%, 1714).
conclusionsThe results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.
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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.