在大学生中使用可解释的机器学习绘制心理风险中的异质性

Penglin Liu¹, Ji Tang¹, Hongxiao Wang¹

¹College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

Entropy (Basel, Switzerland)

|February 27, 2026

概括

这项研究引入了一个新的计算框架,使用可解释AI (XAI) 和无监督学习来识别不同的学生心理健康风险亚型. 这允许比传统的单体方法更个性化的干预措施.

科学领域:

计算心理健康计算心理健康
人工智能在心理学中的应用
高等教育学生福利学生福利

背景情况:

学生心理健康是高等教育中流行后的一个关键问题.
传统的评估往往忽视了风险学生群体的异质性,限制了干预的有效性.
需要先进的方法来理解微妙的心理风险机制.

研究的目的:

开发一个新的计算框架,整合可解释的人工智能 (XAI) 和无监督学习.
解码学生心理风险机制的潜在异质性.
建立针对特定风险驱动因素的精确干预的基础.

主要方法:

使用TreeSHAP和高斯混合模型开发了一个"预测-解释-发现"管道.
根据2556维特征空间 (词汇,语言,情感指标) 确定了不同的风险亚型.
使用前20个核心特征的灵敏度分析验证了鉴定出的机制的结构稳定性.

主要成果:

确定了三种理论基础的风险亚型:学术驱动 (28.46%),社会情绪 (43.85%) 和内部监管 (27.69%).
亚型被验证为定于主要决策驱动因素,而不是高维噪声.
证明了黑盒分类器的转化为诊断工具.

结论:

该框架将计算精神健康中的预测准确性和机械学理解相结合.
结果与研究领域标准 (RDoC) 保持一致,支持精确干预.
在高等教育中推进基于机制的子类型,以提供个性化的学生支持.

关键词:

高斯混合模型的模拟模型.树木SHAP 树木SHAP 树木SHAP 在线基于机制的分类.心理风险异质性心理风险异质性学生心理健康学生心理健康

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