ReviewInternational journal of environmental research and public health2023
Harnessing Machine Learning in Tackling Domestic Violence-An Integrative Review.
Review in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Detecting Patterns of Intimate Partner Violence Using Qualitative Analyses and Machine Learning Algorithms.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Domestic violence in Nepal: Insights from machine learning-based prediction.Journal of public health research · 2026Article
- Article
- Artificial Intelligence in Public Health: Bridging Today's Trends with Tomorrow's Possibilities.Bioengineering (Basel, Switzerland) · 2025Article
- Classifying the Information Needs of Survivors of Domestic Violence in Online Health Communities Using Large Language Models: Prediction Model Development and Evaluation Study.Journal of medical Internet research · 2025Article
- Classification of domestic violence Persian textual content in social media based on topic modeling and ensemble learning.Heliyon · 2024Article
- Harnessing Health Information Technology in Domestic Violence in the United States: A Scoping Review.Public health reviews · 2024Article
- Examining the Supports and Advice That Women With Intimate Partner Violence Experience Received in Online Health Communities: Text Mining Approach.Journal of medical Internet research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research.
methodsWe extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria.
resultsTwelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (
conclusionsLeveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.
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.