用GPT-4提取语言特征，提升阿尔茨海默病早期检测准确率
Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease Detection based on Spontaneous Speech
- 用GPT-4从自发性口语中提取5个语义特征
- 结合传统特征后分类器性能显著提升
- 适用于人工与自动转录文本，适合临床筛查
阿尔茨海默病（AD）是重大且日益严重的公共卫生问题。研究言语与语言模式的改变为大规模、低成本、无创的早期检测提供了可能。大型语言模型（如GPT）为语义文本分析带来了新机遇。本研究利用GPT-4从患者自发性口语转录本中提取五个语义特征，这些特征捕捉了已知的AD症状，但传统计算语言学方法难以有效量化。我们验证了这些特征的临床意义，并对其中一个特征（“找词困难”）通过代理指标和人工评分进行了验证。当结合已有语言特征与随机森林分类器时，基于GPT的特征显著提升了AD检测效果。该方法在人工转录和自动生成转录文本上均表现有效，展示了大语言模型在AD言语分析中的新颖且重要应用。
原文摘要 · Abstract (English)
Alzheimer's Disease (AD) is a significant and growing public health concern. Investigating alterations in speech and language patterns offers a promising path towards cost-effective and non-invasive early detection of AD on a large scale. Large language models (LLMs), such as GPT, have enabled powerful new possibilities for semantic text analysis. In this study, we leverage GPT-4 to extract five semantic features from transcripts of spontaneous patient speech. The features capture known symptoms of AD, but they are difficult to quantify effectively using traditional methods of computational linguistics. We demonstrate the clinical significance of these features and further validate one of them ("Word-Finding Difficulties") against a proxy measure and human raters. When combined with established linguistic features and a Random Forest classifier, the GPT-derived features significantly improve the detection of AD. Our approach proves effective for both manually transcribed and automatically generated transcripts, representing a novel and impactful use of recent advancements in LLMs for AD speech analysis.
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