揭示青少年自杀倾向:使用多种机器学习算法对保护性和风险因素的整体分析
E F Haghish1, Ragnhild Bang Nes2,3, Milan Obaidi4,5
1Department of Psychology, University of Oslo, Oslo, Norway. haghish@uio.no.
Journal of youth and adolescence
|November 20, 2023
概括
一个新的堆叠集团机器学习模型显著改善了青少年自杀企图风险评估. 该模型确定了关键的风险因素,并支持了人际自杀理论,为理解自杀行为提供了更全面的方法.
科学领域:
- 精神病学和心理健康 精神病学和心理健康
- 计算社会科学 计算社会科学
- 公共卫生 公共卫生
背景情况:
- 青少年自杀企图是一个日益严重的公共卫生危机.
- 现有的自杀风险机器学习模型缺乏人口代表性,无法整合保护因素或已建立的自杀理论.
- 合适用于低患病率的堆叠组合算法尚未被评估用于青少年自杀风险评估.
研究的目的:
- 为了比较堆叠组合算法的性能与其他机器学习模型进行青少年自杀企图风险评估.
- 进行整体项目分析,以确定青少年自杀的风险和保护因素.
- 评估已识别的因素与人际自杀理论和自杀菌株理论的兼容性.
主要方法:
- 分析了173,664名挪威青少年 (年龄在13-18岁) 的人口代表数据集.
- 五个机器学习算法,包括一个堆叠组合模型,被训练来预测自杀企图.
- 使用探索性因子分析来确定风险和保护因素,并评估理论兼容性.
主要成果:
- 堆叠组合模型显著优于其他算法,达到90.1%的特异性和67.5%的AUCPR.
- 最近的自我伤害是所有模型中最强的预测因素.
- 还确定了五个额外的风险领域:内化问题,睡眠障碍,饮食失调,缺乏未来乐观,以及受害.
结论:
- 堆叠组合算法显示了改善青少年自杀风险评估在低患病率条件的前景.
- 鉴定的风险因素为人际自杀理论提供了更强大的支持,这表明该理论需要得到改进.
- 综合风险和保护因素的更全面的方法对于理解和预防青少年自杀行为至关重要.
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