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机器学习预测模型用于指导大学生焦虑障碍和抑郁障碍的预防和干预分配 ...
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机器学习预测模型用于指导大学生焦虑障碍和抑郁障碍的预防和干预分配
Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students
——《咨商与发展杂志》第103卷第1期，2025年——
<Journal of Counseling & Development> Volume 103, Issue 1, 2025
【摘要】大学生心理健康一直是专业心理咨询师关注的重点。在过去十年中，焦虑障碍和抑郁障碍变得越来越普遍。利用机器学习（人工智能 (AI) 的一个子集），我们开发了预测模型（即极端梯度提升 [XGBoost]、随机森林、决策树和逻辑回归），以识别可诊断为焦虑障碍和抑郁障碍风险较高的美国大学生。该数据集包括来自133所美国高等教育机构的61,619名学生，并按90:10的比例划分以训练和测试模型。我们采用超参数调整和交叉验证来优化模型性能，并检查了预测性能的多种指标（例如，受试者工作特征曲线下面积 [AUC]、准确度、灵敏度）。结果显示，我们的机器学习预测模型具有很强的判别能力，AUC分别为0.74和0.77，表明当前的财务状况、校园归属感、残疾状况和年龄是焦虑障碍和抑郁障碍的主要预测因素。这项研究为专业心理咨询师提供了一个实用的工具，可以在这些情况恶化之前主动识别患有焦虑障碍和抑郁障碍的学生。机器学习在心理咨询研究中的应用提供了数据驱动的见解，有助于增强对心理健康决定因素的理解，指导预防和干预策略，并通过心理咨询促进不同学生群体的福祉。
【关键词】焦虑、抑郁、机器学习、预测模型、预防和早期干预
[Abstract] College student mental health has been a critical concern for professional counselors. Anxiety and depressive disorders have become increasingly prevalent over the past decade. Utilizing machine learning, a subset of artificial intelligence (AI), we developed predictive models (i.e., eXtreme Gradient Boosting [XGBoost], Random Forest, Decision Tree, and Logistic Regression) to identify US college students at heightened risk of diagnosable anxiety and depressive disorders. The dataset included 61,619 students from 133 US higher education institutions and was partitioned into a 90:10 ratio for training and testing the models. We employed hyperparameter tuning and cross-validation to optimize model performance and examined multiple measures of predictive performance (e.g., area under the receiver operating characteristic curve [AUC], accuracy, sensitivity). Results revealed strong discriminative power in our machine learning predictive models with AUC of 0.74 and 0.77, indicating current financial situation, sense of belonging on campus, disability status, and age as the top predictors of anxiety and depressive disorders. This study provides a practical tool for professional counselors to proactively identify students for anxiety and depressive disorders before these conditions escalate. Application of machine learning in counseling research provides data-driven insights that help enhance the understanding of mental health determinants, guide prevention and intervention strategies, and promote the well-being of diverse student populations through counseling.
[Key words] anxiety, depression, machine learning, predictive model, prevention and early intervention
论文原文：Yusen Zhai, Yixin Zhang, Zhicong Chu, Baocheng Geng, Mahmood Almaawali, Russell Fulmer, Yung-Wei Dennis Lin, Zhaopu Xu, Aubrey D. Daniels, Yanhong Liu, Qu Chen, Xue Du (2025). Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students. Journal of Counseling & Development. Volume 103, Issue 1, Pages 110-125. January 2025.
https://doi.org/10.1002/jcad.12543
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