在不使用与自杀有关的物品的情况下,预测挪威青少年的自杀企图:一种机器学习方法
E F Haghish1, Nikolai O Czajkowski1,2, Tilmann von Soest1,3
1Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway.
Frontiers in psychiatry
|October 12, 2023
概括
这项研究表明,机器学习可以有效地识别处于自杀企图高风险的青少年,而无需使用敏感数据. 关键预测因素包括内化问题,物质使用,人际关系和受害.
科学领域:
- 精神病学是一个精神病学.
- 机器学习 机器学习
- 青少年健康 青少年健康
背景情况:
- 青少年自杀企图的分类通常依赖于敏感数据,这对人口水平的研究构成了挑战.
- 现有的模型在数据收集方面扎,特别是对于青少年.
研究的目的:
- 评估分类高风险青少年自杀企图的可行性,而没有敏感的与自杀有关的调查项目.
- 确定青少年自杀企图的关键预测因素.
主要方法:
- 利用了来自173,664名挪威青少年 (年龄在13-18岁) 的全国调查数据.
- 使用极端梯度提升 (XGBoost) 算法进行二进制分类.
- 分析了169个问卷项目,以确定自杀企图的预测因素.
主要成果:
- XGBoost模型实现了77%的灵敏度和90%的特异性.
- 该模型表现出强的性能,AUC为92.1%和AUPRC为47.1%.
- 确定了内化问题,物质使用,人际关系和受害者作为重要的预测因素.
结论:
- 机器学习提供了一种可行的方法,可以在没有敏感数据的情况下对青少年自杀企图进行人口规模的查.
- 未来的研究应该专注于内化问题,人际关系,受害和自杀行为病因学中的物质使用.
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