机器学习对青少年自杀的预测性能:系统审查和元分析
Lingjiang Liu1,2, Zhiyuan Li2,3, Yaxin Hu1,2
1Department of Psychiatry, North Sichuan Medical College, Nanchong, China.
Journal of medical Internet research
|June 16, 2025
概括
机器学习 (ML) 模型在预测青少年自杀风险方面表现有前途,特别是在自杀企图方面. 需要进一步研究各种数据和外部验证,以增强临床工具.
科学领域:
- 心理健康研究 心理健康研究
- 计算精神病学是一种计算精神病学.
- 青少年健康 青少年健康
背景情况:
- 青少年自杀是一个关键的公共卫生问题,在早期风险识别方面存在挑战.
- 传统的评估工具对自杀风险的预测准确性有限.
- 机器学习 (ML) 提供了改善青少年风险预测的潜力.
研究的目的:
- 系统地评估ML模型的性能,以预测青少年自杀相关的行为.
- 建立一个证据基础,以开发临床适用的基于ML的风险评估工具.
主要方法:
- 进行了一项针对青少年自杀相关行为预测的ML系统审查.
- 在PubMed,Embase,Cochrane和Web of Science数据库中搜索到2024年4月20日.
- 使用c指数的元分析评估了非自杀性自伤 (NSSI),自杀意念和自杀企图的ML准确性.
主要成果:
- 包括42项研究,包括104个ML模型和超过140万名青少年 (11-20岁).
- ML模型显示出强大的预测性能:NSSI的AUC (0.79),自杀念头 (0.77),以及自杀企图 (0.84).
- 自杀企图预测的最高灵敏度 (0.80);NSSI预测的最高特异性 (0.96).
结论:
- 机器学习技术展示了青少年自杀风险的有希望的预测能力,特别是自杀企图.
- 随机森林和极端梯度增强等组合方法显示出卓越的性能.
- 局限性包括主要的内部验证;未来的研究需要外部验证和更大的,多样化的数据集.
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