中国青少年非自杀性自我伤害的纵向机器学习预测:一个前性的多中心队列研究
Xinyu Guo1, Shuyi Liu2, Lihua Jiang3
1Department of Health Policy and Management, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Department of Applied Social Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong Special Administrative Region of China.
Journal of affective disorders
|August 28, 2025
概括
机器学习模型使用纵向数据准确预测青少年非自杀性自我伤害 (NSSI). 关键的保护因素包括精神和情感能力,
科学领域:
- 青少年心理健康
- 心理学中的机器学习
- 公共卫生研究
背景情况:
- 非自杀性自伤 (NSSI) 在青少年中构成重大公共卫生挑战.
- 传统的NSSI预测方法缺乏足够的准确性.
- 纵向数据分析对于理解NSSI发展至关重要.
研究的目的:
- 开发和评估用于预测青少年NSSI的机器学习模型.
- 通过特征重要性分析确定影响NSSI的关键风险和保护因素.
- 通过逐步框架提高NSSI预测的准确性.
主要方法:
- 利用了超过2.5年的3483名中国中小学生的纵向数据.
- 使用七个机器学习算法和五倍交叉验证的渐进式预测框架.
- 应用SMOTE-NC用于类不平衡和SHAP用于模型解释性.
主要成果:
- 随机森林显示出优异的预测性能 (AUROC ≈ 0.85).
- 确定与自杀相关的行为,抑郁,犯罪和焦虑是主要的危险因素.
- 强调精神,情感能力,生活满意度和同情心是重要的保护因素.
结论:
- 为纵向NSSI预测建立了一个强大的,可解释的机器学习框架.
- 积极的儿童发展因素,特别是精神和情感能力,对于减轻NSSI风险至关重要.
- 这些发现支持基于证据的早期干预和预防青少年NSSI的策略.
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