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People can go to great lengths to protect their self-image and present themselves in ways that they want others to see them. Sociologist Erving Goffman presented the idea that a person is like an actor on a stage. Calling his theory dramaturgy, Goffman believed that we use “impression management” to present ourselves to others as we hope to be perceived. Each situation is a new scene, and individuals perform different roles depending on who is present (Goffman, 1959). Think about...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Updated: Sep 9, 2025

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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
PubMed
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.

关键词:
探索性因素分析综合理论模型机器学习非自杀自伤行为SHAP可视化

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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.