标题： 可解释的图-语言建模用于检测青少年非法药物使用 显示英文标题

标题： Interpretable Graph-Language Modeling for Detecting Youth Illicit Drug Use

摘要： 青少年和年轻成年人（TYAs）中的非法药物使用仍然是一个紧迫的公共卫生问题，其流行率上升并对健康和福祉产生长期影响。 为了检测TYAs中的非法药物使用，研究人员分析大型调查，如青少年风险行为调查（YRBS）和国家药物使用与健康调查（NSDUH），这些调查保留了与物质使用相关的丰富的人口统计、心理和环境因素。 然而，现有的建模方法将调查变量独立处理，忽略了它们之间的潜在和相互关联的结构。 为了解决这一局限性，我们提出了LAMI（LAtent relation Mining with bi-modal Interpretability），一种用于检测TYAs中非法药物使用并解释行为风险因素的新联合图-语言建模框架。 LAMI将个体回答表示为关系图，通过专门的图结构学习层学习潜在连接，并整合大语言模型以生成基于图结构和调查语义的自然语言解释。 在YRBS和NSDUH数据集上的实验表明，LAMI在预测准确性方面优于竞争性基线。 可解释性分析进一步表明，LAMI揭示了有意义的行为子结构和心理社会路径，例如家庭动态、同伴影响和与学校相关的困扰，这些与物质使用的已知风险因素一致。 显示英文摘要

摘要： Illicit drug use among teenagers and young adults (TYAs) remains a pressing public health concern, with rising prevalence and long-term impacts on health and well-being. To detect illicit drug use among TYAs, researchers analyze large-scale surveys such as the Youth Risk Behavior Survey (YRBS) and the National Survey on Drug Use and Health (NSDUH), which preserve rich demographic, psychological, and environmental factors related to substance use. However, existing modeling methods treat survey variables independently, overlooking latent and interconnected structures among them. To address this limitation, we propose LAMI (LAtent relation Mining with bi-modal Interpretability), a novel joint graph-language modeling framework for detecting illicit drug use and interpreting behavioral risk factors among TYAs. LAMI represents individual responses as relational graphs, learns latent connections through a specialized graph structure learning layer, and integrates a large language model to generate natural language explanations grounded in both graph structures and survey semantics. Experiments on the YRBS and NSDUH datasets show that LAMI outperforms competitive baselines in predictive accuracy. Interpretability analyses further demonstrate that LAMI reveals meaningful behavioral substructures and psychosocial pathways, such as family dynamics, peer influence, and school-related distress, that align with established risk factors for substance use.