用语音分析自动发现阿尔茨海默病早期标志，可解释且能发现新关联。
A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease
- 基于预训练模型从语音中提取临床变量，构建贝叶斯网络
- 恢复已有医学知识，并发现语言特征间的新关联关系
- 适合临床辅助诊断与认知障碍研究者使用
识别可靠的阿尔茨海默病（AD）标志通常需要人工、耗时的转录和专家分析，限制了其应用规模。我们提出一种自动化流程，直接从言语流畅性测试的音频记录中提取潜在的AD进展指标的定性知识。该方法利用预训练基础模型处理原始音频，提取具有临床意义的变量，并构建贝叶斯网络（BN）；该网络用于推理AD进展标志及其定性关系。系统成功恢复了已知的临床知识，并发现了语言标记之间的新关系。
原文摘要 · Abstract (English)
Identifying reliable Alzheimer's disease (AD) markers typically requires manual, labor-intensive transcription and expert analysis, limiting its scale. We introduce an automated pipeline that extracts qualitative knowledge about potential AD progression indicators directly from audio recordings of verbal fluency tests. Our method uses pretrained foundation models to process raw audio and extract clinically relevant variables to construct a Bayesian Network (BN); this BN is used to reason about the AD progression markers and infer their qualitative relationships. Our system successfully recovers known clinical knowledge and identifies novel relationships between linguistic markers.
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