利用大语言模型推理增强的患者转录分析用于阿尔茨海默病检测
Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection
AI总结：
提出利用大语言模型推理增强的患者转录分析框架，系统提取语言缺陷属性并整合到Albert模型，在ADReSS数据集上显著提升AD检测准确率和F1，增强可解释性。
摘要：
阿尔茨海默病（AD）是痴呆症的主要原因，其特征是言语和语言能力的逐渐衰退。近年来深度学习的进展促进了通过自发语音进行自动化AD检测。然而，常见的基于转录的检测方法直接对每个话语中的文本模式进行建模，缺乏对患者语言特征的整体视角，导致判别性和可解释性有限。尽管大语言模型（LLMs）具有增强的推理能力，但在充分利用推理能力以促进AD检测和模型解释方面仍存在差距。因此，我们提出了一种基于患者级别的转录分析框架，利用基于LLM的推理增强来系统性地引出语言缺陷属性。这些属性的汇总嵌入被整合到Albert模型中进行AD检测。与未使用推理增强的基线相比，该框架在ADReSS数据集上实现了8.51%的准确率提升和8.34%的F1提升。我们的进一步分析显示了所识别的语言缺陷属性的有效性，以及利用LLM进行AD检测解释的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
