大学生自杀意念、计划与未遂的预测：理论驱动下的机器学习模型构建
Prediction of Suicidal Ideation, Suicide Plans, and Suicide Attempts Among College Students: Developing a Theory-Driven Machine Learning Model
陈慧敏 1王文超 2刘鹿鸣 1张立颖 1李洋 2伍新春2
作者信息
- 1. 北京师范大学心理学部，应用实验心理北京市重点实验室，北京，100875
- 2. 北京师范大学心理学部，应用实验心理北京市重点实验室，北京，100875;北京师范大学湾区应用心理学院，珠海，519087
- 折叠
摘要
大学生自杀已成为日益严峻的公共卫生问题。传统统计方法难以应对自杀理论中大量预测因子及其复杂交互作用，而现有机器学习研究常缺乏理论指导，且较少同时比较不同自杀相关结局的预测特征。为弥补上述不足，本研究采用基于理论的机器学习方法，以自杀叙事-危机模型为框架，利用两次独立调查获得的大学生数据（共8203人），分别构建自杀意念、计划与未遂三类结局的机器学习预测模型，系统比较不同特征在三类结局中的预测贡献，并据此筛选出关键变量构建简化模型。结果发现，烟酒使用在自杀计划和自杀未遂模型中的相对预测贡献更高，而关系欺凌、情感虐待等人际创伤变量在自杀意念模型中的相对贡献更突出。基于16项关键特征构建的简化模型在内部测试和同校时间验证中仍表现出较好的区分能力。本研究表明，理论指导的变量选择与机器学习方法相结合，有助于比较不同自杀相关结局的同期预测特征，并为自杀风险评估提供参考。
Abstract
Suicide among college students has emerged as an increasingly urgent public health issue. Traditional statistical methods can face challenges when handling numerous predictors and complex interactions among them, whereas existing machine learning studies frequently lack theoretical grounding and often focus on a single suicide-related outcome. To address these gaps, this study adopted the Narrative-Crisis Model of Suicide as a theoretical framework and developed separate predictive models for suicidal ideation, suicide plans, and suicide attempts, with the aim of comparing the predictive contributions of features across these three suicide-related outcomes. Following feature importance analysis, simplified models were constructed and further evaluated in a same-school temporal validation sample.This study drew on two large-scale survey datasets collected from Chinese university students. In Dataset 1 (n = 5,296), baseline models were developed using 74 features primarily derived from the Narrative-Crisis Model of Suicide, supplemented by variables informed by related theoretical frameworks. Random Forest and Extreme Gradient Boosting were used for model training, with a linear support vector machine included as a supplementary comparator. Random Forest was used for the primary feature contribution analyses. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied to the training data. Model performance was evaluated across 10 repeated runs using AUC, AUPRC, accuracy, precision, recall, specificity, and F1 score. Based on SHapley Additive exPlanations (SHAP) values and feature importance rankings across the three outcome models, 16 variables were retained to construct simplified models, which were evaluated on both the internal hold-out test set and a same-school temporal validation sample collected at a later time point. All three suicide-related outcomes were assessed over the six months preceding each survey.The results showed distinct predictive patterns across the three suicide-related outcomes. Tobacco and alcohol use showed relatively greater predictive contributions in the suicide plan and suicide attempt models than in the suicidal ideation model, whereas interpersonal trauma, including verbal and relational bullying, emotional abuse, and domestic violence, showed relatively greater contributions in the suicidal ideation model. Variables related to suicide crisis syndrome, suicidal narratives, and self-harm behavior also ranked highly across the models. Despite a substantial reduction in the number of predictors, the simplified models maintained good discrimination in both internal testing and same-school temporal validation. In the temporal validation sample, suicide plans and attempts were reported by only 25 and 23 participants, respectively, warranting caution when interpreting their high AUC estimates. Supplementary analyses further indicated that calibration remained imperfect and that decision-analytic performance varied across risk thresholds.Overall, the findings suggest that combining theory-informed variable selection with data-driven machine learning may help identify outcome-specific predictive patterns across suicidal ideation, suicide plans, and suicide attempts. The simplified feature set may provide a useful basis for further validation and refinement of suicide risk assessment models in college populations.关键词
自杀预测/机器学习/自杀叙事-危机模型/大学生Key words
suicide prediction/machine learning/Narrative-Crisis Model of Suicide/college students引用本文复制引用
陈慧敏,王文超,刘鹿鸣,张立颖,李洋,伍新春.大学生自杀意念、计划与未遂的预测：理论驱动下的机器学习模型构建[EB/OL].(2026-11-10)[2026-11-14].https://chinaxiv.org/abs/202611.00102.学科分类
教育
