用语音特征判断精神分裂症症状严重程度，助力资源有限地区优先救治。
Speech-Based Prioritization for Schizophrenia Intervention
- 基于语音的双人比较模型，利用发音和声学特征评估症状
- 在排序指标上超越传统回归模型，提升分诊准确性
- 适合临床资源紧张地区远程持续监测，减轻评估负担
全球数百万患者受心理健康问题困扰，但因临床资源有限和评估耗时，大量患者未能及时诊断或治疗。现有机器辅助方法多聚焦于诊断分类，而症状严重程度评估对资源受限环境下的医疗优先级划分至关重要。语音驱动的AI提供可扩展的自动化、连续化、远程监测方案，减少对主观自述和人工评估的依赖。本文提出一种基于语音的双人比较模型，利用发音与声学特征进行症状严重程度对比，并通过Bradley-Terry模型生成严重程度排名。实验表明，该方法在排序类指标上优于以往的回归模型，为临床分诊与优先级管理提供了更有效解决方案。
原文摘要 · Abstract (English)
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.
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