一种用于阿尔茨海默病可解释早期诊断的神经符号方法
A Neurosymbolic Approach for Explainable Early Diagnosis of Alzheimer's Disease
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中文总结 AI 辅助
该研究提出一种自动化流程，用预训练基础模型处理言语流畅性测试音频构建贝叶斯网络，以实现阿尔茨海默病的可解释早期诊断，成功恢复临床知识并识别语言标志物间新关系。
中文摘要 AI 辅助
识别可靠的阿尔茨海默病（AD）标志物通常需要人工、劳动密集型的转录和专家分析，限制了其应用规模。我们引入了一种自动化流程，可直接从言语流畅性测试的音频记录中提取潜在AD进展指标的定性知识。我们的方法使用预训练的基础模型处理原始音频，提取临床相关变量以构建贝叶斯网络（BN）；该BN用于推理AD进展标志物并推断它们的定性关系。我们的系统成功恢复了已知的临床知识，并识别出语言标志物之间的新关系。
英文摘要
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.
