标题： 基于语音的干预优先级排序 显示英文标题

标题： Speech-Based Prioritization for Schizophrenia Intervention

摘要： 数以百万计的人遭受心理健康状况的影响，但由于临床资源有限和评估方法劳动密集，许多人未被诊断或接受延迟治疗。 虽然大多数机器辅助方法侧重于诊断分类，但在资源有限的环境中，估计症状严重程度对于优先安排护理至关重要。 基于语音的AI提供了一种可扩展的替代方案，通过实现自动化、连续和远程监测，减少对主观自我报告和耗时评估的依赖。 在本文中，我们介绍了一种基于语音的模型，用于精神分裂症症状严重程度的成对比较，利用了发音和声学特征。 这些比较通过Bradley-Terry模型生成严重程度排名。 我们的方法在基于排名的指标上优于以前的回归模型，为临床分诊和优先处理提供了更有效的解决方案。 显示英文摘要

摘要： Millions of people suffer from mental health conditions, yet many remain undiagnosed or receive delayed care due to limited clinical resources and labor-intensive assessment methods. While most machine-assisted approaches focus on diagnostic classification, estimating symptom severity is essential for prioritizing care, particularly in resource-constrained settings. Speech-based AI provides a scalable alternative by enabling automated, continuous, and remote monitoring, reducing reliance on subjective self-reports and time-consuming evaluations. In this paper, we introduce a speech-based model for pairwise comparison of schizophrenia symptom severity, leveraging articulatory and acoustic features. These comparisons are used to generate severity rankings via the Bradley-Terry model. Our approach outperforms previous regression-based models on ranking-based metrics, offering a more effective solution for clinical triage and prioritization.