解码青少年非自杀性自我伤害:理解可解释的机器学习见解
Haojie Fu1,2, Mengmeng Zhang3, Shuran Yang4
1Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Siping Road, Shanghai, 200092, Shanghai, China.
BMC public health
|September 1, 2025
概括
机器学习模型有效地识别青少年非自杀性自伤 (NSSI) 风险因素. 关键因素包括焦虑,抑郁,自尊和人际关系问题,完善了综合理论模型.
科学领域:
- 青少年心理学
- 计算精神病学
- 行为科学
背景情况:
- 非自杀性自我伤害 (NSSI) 是一个普遍但具有挑战性的青少年风险行为.
- 早期发现和干预对于减轻NSSI的影响至关重要.
- 了解潜在的风险和保护因素对于制定有效策略至关重要.
研究的目的:
- 为青少年NSSI开发可解释的机器学习分类模型.
- 确定与NSSI相关的关键风险和保护因素.
- 在综合理论模型的框架内评估这些因素.
主要方法:
- 通过问卷收集来自中国东部2989名青少年的数据
- 应用了六种机器学习算法:KNN,SVM,物流回归,LGBM,CatBoost,XGBoost.
- 用于确定关键因素的SHAP可视化和探索因素分析.
主要成果:
- CatBoost算法显示出优异的性能 (AUPRC=0. 736,AUC=0. 863).
- SHAP分析强调了23个影响NSSI的重要项目.
- 发现了七种因素:情境焦虑,抑郁症状,积极的日常运作,负面的自尊,自我评价的行为,欺凌和反应性攻击,以及人际关系问题和自我接受.
结论:
- 机器学习为分析复杂的NSSI数据提供了强大的方法.
- 这些因素为完善NSSI综合理论模型提供了洞察力.
- 这项研究提高了对青少年NSSI的理解,有助于针对性干预.
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