用视觉大模型和词频逆文档频率，打造可解释的阿尔茨海默病语音筛查特征。
Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease
- 结合大语言模型视觉能力与TF-IDF构建可解释语音特征
- 在两个分类器上均超越传统语言特征，维度效率高
- 适合关注临床可解释性的医疗AI研究者
阿尔茨海默病（AD）已成为老龄化社会的重大健康挑战。基于语音描述的检测方法因其可扩展性日益普及。本文以饼干盗窃图片描述任务为基础，提出一种融合大型语言模型（LLM）视觉能力与词频逆文档频率（TF-IDF）的可解释、高效特征集。实验表明，新特征在两种不同分类器上均持续优于传统语言特征，且具有高维度效率。该特征集可逐步解释，显著提升自动阿尔茨海默病筛查的可解释性。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society. The use of spoken language-based AD detection methods has gained prevalence due to their scalability due to their scalability. Based on the Cookie Theft picture description task, we devised an explainable and effective feature set that leverages the visual capabilities of a large language model (LLM) and the Term Frequency-Inverse Document Frequency (TF-IDF) model. Our experimental results show that the newly proposed features consistently outperform traditional linguistic features across two different classifiers with high dimension efficiency. Our new features can be well explained and interpreted step by step which enhance the interpretability of automatic AD screening.
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