Articlenpj women's health2026
Leveraging multimodal machine learning for accurate risk identification of intimate partner violence.
Article in npj women's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis 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
3 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
- Non-medical drivers of health and imaging patterns in intimate partner violence.Emergency radiology · 2026Article
- AI-Enabled First-Response Support After Sexual and Gender-Based Violence: A PRISMA-ScR Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Intimate partner violence (IPV) refers to the abuse from previous or current partners. It is a widespread but underreported public health concern that has a wide range of negative effects on the physical and mental health of those affected. This work presents machine learning models for the early detection of IPV in clinical settings, developed with a dataset of female patients who sought help at a domestic abuse intervention and prevention center of a major hospital in the United States. Utilizing tabular clinical data and unstructured clinical notes, we build single-modality and multimodal models for different data availability scenarios. Our multimodal model can identify patients at risk of IPV with an AUC of 0.88 and years before patients seek help. We validated the model on patients who did not seek help at the intervention center and patients from another hospital in the same integrated network with comparable performance.
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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 Socratic graph.