基于ABCD研究的青少年物质使用 onset 的纵向与图增强预测
Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study
AI总结：
本研究基于ABCD研究数据，对比多种模型预测青少年物质使用 onset，发现时间XGBoost表现最优，结合其与T-GCN预测的堆叠模型性能最佳，揭示了纵向建模及图表示的预测价值。
AI中文摘要：
早期识别青少年物质使用风险是一项重要的预防挑战，但基线特征、纵向轨迹和关系背景的相对价值仍不明确。本研究使用青少年脑认知发展（Adolescent Brain Cognitive Development, ABCD）研究中约11860名参与者的数据，比较了横断面、纵向和基于图的方法，用于预测少量饮酒、饮酒、吸食大麻及饮酒/吸食大麻的情况。我们评估了基于树的模型、循环神经网络（RNN）以及由家庭、学校和特征相似性图构建的时间图卷积网络（Temporal Graph Convolutional Networks, T-GCNs）。纵向模型的表现始终优于基线模型，其中时间XGBoost（temporal XGBoost）取得了最强的独立表现。尽管T-GCNs总体上未超过时间XGBoost，但图衍生的风险评分提供了补充信息。通过评分级堆叠（score-level stacking）结合时间XGBoost与T-GCN的预测，在所有结局中均取得了最佳表现，AUC-ROC值超过0.79。特征分析确定了同伴偏差、年龄、外化症状、父母监管、文化规范和邻里环境是物质使用 onset 的重要预测因子。这些发现证明了纵向建模在物质使用预测中的价值，并表明基于图的表示可以提供有效的辅助风险信号。
英文摘要：
Early identification of adolescent substance-use risk is an important prevention challenge, yet the relative value of baseline characteristics, longitudinal trajectories, and relational context remains unclear. Using data from approximately 11,860 participants in the Adolescent Brain Cognitive Development (ABCD) Study, we compare cross-sectional, longitudinal, and graph-based approaches for predicting alcohol sipping, alcohol use, marijuana use, and alcohol/marijuana use. We evaluate tree-based models, recurrent neural networks, and Temporal Graph Convolutional Networks (T-GCNs) constructed from family, school, and feature-similarity graphs. Longitudinal models consistently outperform baseline models, with temporal XGBoost achieving the strongest standalone performance. Although T-GCNs generally do not surpass temporal XGBoost, graph-derived risk scores provide complementary information. Combining temporal XGBoost and T-GCN predictions through score-level stacking yields the best performance across all outcomes, achieving AUC-ROC values above 0.79. Feature analyses identify peer deviance, age, externalizing symptoms, parental monitoring, cultural norms, and neighborhood context as important predictors of substance use onset. These findings demonstrate the value of longitudinal modeling for substance-use prediction and suggest that graph-based representations can provide effective auxiliary risk signals.
