预测性风险建模用于儿童虐待发现和增强决策:来自丹麦行政数据的证据
Michael Rosholm1,2,3,4, Simon Tranberg Bodilsen1,2,4, Bastien Michel2,5
1Department of Economics and Business Economics, Aarhus University, Aarhus, Denmark.
PloS one
|July 10, 2024
概括
预测模型准确地识别了有虐待风险的儿童,有助于儿童保护服务. 这些工具改善了案例工作者的决策,使得更早的干预和更好的结果对弱势儿童.
科学领域:
- 公共卫生 公共卫生
- 社会工作 社会工作 社会工作
- 数据科学数据科学数据科学
背景情况:
- 虐待儿童带来了重大的社会和个人成本.
- 有效地识别有风险的儿童对于及时干预至关重要.
- 现有的儿童保护风险评估方法可能存在局限性.
研究的目的:
- 开发和评估机器学习模型,用于预测儿童移除决策.
- 评估这些模型在识别有虐待风险的儿童方面的准确性.
- 检查这些模型的潜力,以支持儿童保护案件工作者的决策.
主要方法:
- 使用丹麦行政数据进行的回顾性队列研究 (2016年4月 - 2017年12月).
- 分析了涉及102,309名儿童的195,639起转诊情况.
- 实施和比较四个机器学习模型与广泛的儿童和家庭背景数据.
主要成果:
- 表现最好的模型获得了AUC-ROC得分超过87%,证明了强大的预测能力.
- 模型预测与儿童不良结果 (犯罪,健康问题,缺勤) 有积极的相关性.
- 预测模型显示,有可能减少分类错误,并帮助早期识别有风险的儿童.
结论:
- 机器学习模型可以有效地预测儿童移除决策,并识别有风险的儿童.
- 这些模型可以增强案例工作者的决策,从而导致更早的干预.
- 利用预测性风险模型提供了一个有希望的方法来改善弱势儿童的结果.
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