预测精神病父母青少年的虐待:一个随机森林树分析
Sarah-Louise Unterschemmann1, Hanna Christiansen2,3, Beate Kettemann2
1Department of Psychology, Marburg University, Gutenbergstr. 18, 35032, Marburg, Germany. unterscs@students.uni-marburg.de.
Child psychiatry and human development
|November 11, 2025
概括
患有精神疾病的父母的孩子面临更高的虐待风险. 机器学习模型显示,使用父母和儿童报告对儿童虐待的预测中等至良好,有助于早期干预.
科学领域:
- 精神病学和心理学 精神病学和心理学
- 儿童发展 儿童发展
- 在医疗保健中的数据科学.
背景情况:
- 患有精神疾病的父母的孩子面临患上精神疾病和遭受虐待的风险较高.
- 在这个弱势群体中早期识别虐待预测因素对于及时的支持和干预至关重要.
- 现有研究强调了心理健康状况和不良童年经历对代际影响.
研究的目的:
- 调查在有精神病父母的家庭中儿童虐待的可预测性.
- 通过使用家长和孩子的自我报告来评估机器学习模型在预测虐待儿童方面的有效性.
- 确定危险儿童虐待早期风险评估的关键预测因素.
主要方法:
- 采用随机森林分类器来分析精神病医院住院患者及其子女的数据.
- 儿童虐待症状的评估是使用童年创伤问卷简短形式进行的.
- 开发了三种预测模型:父母估计的儿童创伤,父母报告的儿童虐待和儿童自我报告的虐待.
主要成果:
- 模型1 (家长估计的创伤) 达到76.62%的准确性,曲线下的面积 (AUC) 为0.85.
- 模型3 (儿童自我报告的虐待) 显示了73.68%的准确性,AUC为0.84.
- 模型2 (父母对孩子自我评估虐待的数据) 显示精度为68.42%,AUC为0.69,表明适度的可预测性.
结论:
- 机器学习模型为评估父母患有精神疾病的家庭中的虐待儿童提供了中等到良好的可预测性.
- 父母和孩子的自我报告是评估风险的宝贵数据来源,父母估计的创伤和孩子的自我报告显示出更高的准确性.
- 这些发现为开发针对性干预和为这些家庭中面临风险儿童提供支持系统提供了初步见解.
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