使用机器学习预测自杀念头或非自杀自伤的儿童中首次自杀企图的发生:基于前性人口的队列研究
Chuncheng Huang1, Yinan Zhou2, Yanling Yue1
1Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China.
American journal of epidemiology
|October 30, 2025
概括
在有自杀念头或非自杀自伤 (NSSI) 的儿童中,确定首次自杀企图的预测因素至关重要. 照顾者报告的NSSI,目睹家庭暴力和焦虑障碍增加了这些年轻人的自杀企图风险.
科学领域:
- 儿童和青少年精神病学
- 发展心理学 发展心理学
- 公共卫生 公共卫生
背景情况:
- 具有自杀念头或非自杀自伤 (NSSI) 的儿童面临自杀风险升高.
- 然而,大多数这些儿童并没有尝试自杀,因此需要确定特定的危险因素.
研究的目的:
- 确定儿童中首次自杀企图的预测因素,这些儿童经历了自杀念头或NSSI.
- 为儿童制定有针对性的,基于人口的自杀预防策略提供信息.
主要方法:
- 分析了来自青少年大脑和认知发展 (ABCD) 研究的纵向数据.
- 机器学习模型,特别是离散时间随机生存森林,用于识别预测因素.
- 使用双变特征选择来完善预测模型.
主要成果:
- 在有自杀念头的儿童中,自杀企图风险增加与照顾者报告的NSSI有关,目睹家庭暴力,自杀思想严重性,女性性,在线屏幕使用和父母监督减少.
- 对于患有NSSI的儿童,自杀企图的危险因素包括目睹家庭暴力,焦虑障碍,照顾者报告的NSSI,女性性别以及破坏性/冲动控制/行为障碍.
- 自杀企图发生在11.6%的自杀念头儿童和12.3%的NSSI儿童在4年的随访期间.
结论:
- 特定的人口,环境和临床因素预测了自杀念头或NSSI儿童的第一次自杀企图.
- 研究结果强调了在自杀风险评估中考虑照顾者的行为,接触暴力,心理健康状况和在线活动的重要性.
- 这些见解可以指导为有风险的儿童创建有效的,基于证据的自杀预防计划.
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