基于数据的儿童忽视和虐待的预测,使用综合的市政来源
Naama Parush Shear Yashuv1, Rinat Salem2, Ofra Abramson2
1KI Research Institute, Kfar-Malal, Israel.
Child abuse & neglect
|January 8, 2026
概括
使用集成市政数据的机器学习模型可以预测儿童被忽视和虐待风险. 这种方法增强了对有风险儿童的早期发现和干预.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 社会工作 社会工作 社会工作
背景情况:
- 儿童被忽视和虐待是具有持久健康后果的重大全球问题.
- 市政数据有潜力识别有风险的儿童,但往往是孤立的.
- 机器学习可以将这些数据汇总起来,用于早期检测和干预.
研究的目的:
- 评估机器学习模型在预测儿童被忽视和虐待风险方面的准确性.
- 评估综合市政数据在大量儿童人口中用于风险预测的使用情况.
- 为了识别有风险的儿童,及时进行干预.
主要方法:
- 利用了超过47万名儿童的非身份化数据集,这些儿童在市政系统中相互联系.
- 使用儿童福利记录定义了忽视和虐待结果.
- 开发模型以利用各种数据源预测当前和未来 (2年) 的风险.
主要成果:
- 模型取得了良好的表现 (AUC 0.75-0.88),通过整合数据 (教育,税收) 得到改善.
- 在被确定的有风险儿童中,前5%的儿童包括32-34%的未来被忽视/虐待案件,直到2年前.
- 两性成绩一致,但阿拉伯儿童的成绩略低于犹太儿童.
结论:
- 多种来源的市政数据和机器学习有效地识别了有虐待风险的儿童.
- 这些工具可以增强早期检测,资源分配和弱势儿童的结果.
- 负责任的实施需要道德考虑,文化敏感性和人类监督.
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