标题： MAQuA：使用项目反应理论进行多维心理健康筛查的自适应提问 显示英文标题

标题： MAQuA: Adaptive Question-Asking for Multidimensional Mental Health Screening using Item Response Theory

摘要： 大型语言模型（LLMs）的最新进展为可扩展的、交互式的心理健康评估提供了新的机会，但LLMs的过度查询会增加用户的负担，并且在跨诊断症状谱的现实筛查中效率低下。 我们引入了MAQuA，这是一种自适应的问题提问框架，用于同时进行多维心理健康筛查。 结合语言响应上的多结果建模与项目反应理论（IRT）和因子分析，MAQuA在每一步选择在多个维度上具有最丰富信息响应的问题，以优化诊断信息，提高准确性并可能减少回答负担。 在新数据集上的实证结果表明，与随机排序相比，MAQuA将达到分数稳定所需的评估问题数量减少了50-87%（例如，用71%更少的问题达到稳定的抑郁评分，用85%更少的问题达到稳定的食物障碍评分）。 MAQuA在内化（抑郁、焦虑）和外化（物质使用、食物障碍）领域都表现出稳健的性能，早期停止策略进一步减少了患者的耗时和负担。 这些发现使MAQuA成为一种强大而高效的工具，用于可扩展、细致和交互式的心理健康筛查，推动基于LLM的代理在现实临床工作流程中的整合。 显示英文摘要

摘要： Recent advances in large language models (LLMs) offer new opportunities for scalable, interactive mental health assessment, but excessive querying by LLMs burdens users and is inefficient for real-world screening across transdiagnostic symptom profiles. We introduce MAQuA, an adaptive question-asking framework for simultaneous, multidimensional mental health screening. Combining multi-outcome modeling on language responses with item response theory (IRT) and factor analysis, MAQuA selects the questions with most informative responses across multiple dimensions at each turn to optimize diagnostic information, improving accuracy and potentially reducing response burden. Empirical results on a novel dataset reveal that MAQuA reduces the number of assessment questions required for score stabilization by 50-87% compared to random ordering (e.g., achieving stable depression scores with 71% fewer questions and eating disorder scores with 85% fewer questions). MAQuA demonstrates robust performance across both internalizing (depression, anxiety) and externalizing (substance use, eating disorder) domains, with early stopping strategies further reducing patient time and burden. These findings position MAQuA as a powerful and efficient tool for scalable, nuanced, and interactive mental health screening, advancing the integration of LLM-based agents into real-world clinical workflows.