用大模型推理增强患者语言特征分析，提升阿尔茨海默病检测效果
Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection
- 基于大模型推理提取患者整体语言缺陷特征
- 在ADReSS数据集上准确率提升8.51%，F1提升8.34%
- 适合关注临床可解释性与生成式AI辅助诊断的研究者
阿尔茨海默病（AD）是痴呆的主要原因，表现为语言能力的渐进性衰退。近年来深度学习推动了通过自发言语实现自动AD检测的发展。然而，现有基于文本的检测方法仅关注单个语句的模式，缺乏对患者整体语言特征的全局视角，导致判别力和可解释性不足。尽管大语言模型（LLM）具备更强的推理能力，但其在辅助AD检测与模型解释方面的潜力尚未被充分挖掘。为此，我们提出一种基于大模型推理增强的患者级转录本画像框架，系统性地提取语言缺陷属性。将这些属性的摘要嵌入Albert模型以实现AD检测。该框架在ADReSS数据集上相比基线模型，准确率提升8.51%，F1值提升8.34%。进一步分析表明，所识别的语言缺陷属性有效，且展示了利用LLM进行AD检测解释的潜力。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) stands as the predominant cause of dementia, characterized by a gradual decline in speech and language capabilities. Recent deep-learning advancements have facilitated automated AD detection through spontaneous speech. However, common transcript-based detection methods directly model text patterns in each utterance without a global view of the patient's linguistic characteristics, resulting in limited discriminability and interpretability. Despite the enhanced reasoning abilities of large language models (LLMs), there remains a gap in fully harnessing the reasoning ability to facilitate AD detection and model interpretation. Therefore, we propose a patient-level transcript profiling framework leveraging LLM-based reasoning augmentation to systematically elicit linguistic deficit attributes. The summarized embeddings of the attributes are integrated into an Albert model for AD detection. The framework achieves 8.51\% ACC and 8.34\% F1 improvements on the ADReSS dataset compared to the baseline without reasoning augmentation. Our further analysis shows the effectiveness of our identified linguistic deficit attributes and the potential to use LLM for AD detection interpretation.
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