Data-driven prediction of child neglect and abuse using integrated municipal sources

Machine learning models utilizing multi-source municipal data can effectively identify children at risk of maltreatment. Such tools may support municipal welfare systems by enhancing early detection, guiding resource allocation, and improving outcomes for vulnerable children. However, ethical considerations, cultural sensitivity, and human oversight are essential to ensure responsible implementation.

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Publication Details

Journal
Child Abuse & Neglect
Published
2026-01-07
DOI
https://doi.org/10.1016/j.chiabu.2025.107872
Primary Topic
Child Abuse and Trauma
Type
article
Field-Weighted Citation Impact
0.00
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article

Data-driven prediction of child neglect and abuse using integrated municipal sources

Guy Amit, Rinat Salem, Pini Akiva, Ben Y. Reis et al.
Child Abuse & Neglect
Child Abuse and Trauma
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Data-driven prediction of child neglect and abuse using integrated municipal sources

Guy Amit, Rinat Salem, Pini Akiva, Ben Y. Reis, Naama Parush Shear Yashuv, Ofra Abramson, Naama Holzman, Avrum Neumbourg, Astar Sobol, Merav Dadia-Molad, Ayelet Carmeli-Messinger, Maytal Bivas-Benita, Amir Ron
article en

Abstract

Machine learning models utilizing multi-source municipal data can effectively identify children at risk of maltreatment. Such tools may support municipal welfare systems by enhancing early detection, guiding resource allocation, and improving outcomes for vulnerable children. However, ethical considerations, cultural sensitivity, and human oversight are essential to ensure responsible implementation.

Child Abuse & NeglectVol. 172
Boston Children's Hospital (US), IMI TAMI Institute for Research and Development (IL), Charles and Lynn Schusterman Family Foundation (US), Academic College of Tel Aviv-Yafo (IL)
No poverty
Openalex Percentile: Top 96%
Child Abuse and Trauma
DOIOpen Access PDFOpenAlex
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