Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physicians, which is often time-consuming and resource-intensive in terms of both human expertise and healthcare resources. In recent years, large language models (LLMs) have been increasingly applied to the medical field using electronic health records (EHRs), yet their application in Alzheimer's disease assessment remains limited, particularly given that AD involves complex multifactorial etiologies that are difficult to observe directly through imaging modalities. In this work, we propose leveraging LLMs to perform Chain-of-Thought (CoT) reasoning on patients' clinical EHRs. Unlike direct fine-tuning of LLMs on EHR data for AD classification, our approach utilizes LLM-generated CoT reasoning paths to provide the model with explicit diagnostic rationale for AD assessment, followed by structured CoT-based predictions. This pipeline not only enhances the model's ability to diagnose intrinsically complex factors but also improves the interpretability of the prediction process across different stages of AD progression. Experimental results demonstrate that the proposed CoT-based diagnostic framework significantly enhances stability and diagnostic performance across multiple CDR grading tasks, achieving up to a 15% improvement in F1 score compared to the zero-shot baseline method.

翻译：阿尔茨海默病（AD）已成为全球范围内普遍存在的神经退行性疾病。传统诊断方法仍严重依赖医学影像和医生的临床评估，这在专业人力资源和医疗资源方面通常耗时且耗费巨大。近年来，大语言模型（LLMs）在利用电子健康记录（EHRs）的医疗领域应用日益增多，但它们在阿尔茨海默病评估中的应用仍然有限，尤其是考虑到AD涉及复杂的多因素病因，难以通过影像学模式直接观察。在本研究中，我们提出利用LLMs对患者的临床EHRs进行思维链（CoT）推理。与直接在EHR数据上对LLMs进行AD分类的微调不同，我们的方法利用LLM生成的CoT推理路径为模型提供明确的AD评估诊断依据，随后进行基于结构化CoT的预测。该流程不仅增强了模型诊断内在复杂因素的能力，还提高了AD进展不同阶段预测过程的可解释性。实验结果表明，所提出的基于CoT的诊断框架在多项CDR分级任务中显著提升了稳定性和诊断性能，与零样本基线方法相比，F1分数最高提升了15%。