在青春期中期和晚期,使用机器学习对自杀企图的年龄分层预测
Karen Kusuma1, Mark Larsen1, Juan C Quiroz1
1University of New South Wales, Sydney, NSW 2052, Australia.
Journal of affective disorders
|August 14, 2024
概括
预测自杀企图需要特定年龄的模型,因为机器学习方法显示了青春期中期 (与学校有关的因素) 与青春期晚期 (之前的自杀倾向,心理健康) 的不同预测因素. 这些发现强调了在发育阶段进行量身定制的自杀风险评估的必要性.
科学领域:
- 青少年心理学 青少年心理学
- 医疗保健中的机器学习
- 公共卫生监督 公共卫生监督
背景情况:
- 自杀行为患病率在青春期中期和晚期升级.
- 了解自杀行为的特定年龄表现对于有效的干预至关重要.
- 这项研究解决了在青少年发育阶段需要不同的预测模型的需求.
研究的目的:
- 开发和比较机器学习模型,用于预测青春期中期 (14-15岁) 和青春期末 (18-19岁) 的自杀企图.
- 确定青少年不同发育阶段的自杀行为年龄特定预测因素.
- 检查青春期中期和后期之间的预测模型性能差异.
主要方法:
- 利用了来自澳大利亚儿童长度研究 (N=2266) 的数据.
- 综合自我报告问卷,医疗保健和教育行政数据,用于700多个潜在预测因素.
- 使用后勤回归,随机森林和梯度增强算法进行预测建模.
主要成果:
- 与青春期中期的模型 (AUROC 0.70-0.76) 相比,青春期后期的模型表现更好 (AUROC 0.77-0.88).
- 青春期中期的关键预测因素主要与学校有关.
- 在青春晚期,先前的自杀倾向,心理健康和未来计划成为重要预测因素.
结论:
- 这项研究开创了机器学习用于青少年特定年龄的自杀试图预测.
- 这些发现强调,最佳的自杀风险预测模型是青春期的阶段依赖.
- 未来的研究和干预措施必须承认青春期自杀风险呈现的动态性质.
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