Leveraging Large Language Models for Identifying Interpretable Linguistic Markers and Enhancing Alzheimer's Disease Diagnostics

Abstract

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disorder. Early detection of AD is crucial for timely disease intervention. This study proposes a novel LLM framework, which extracts interpretable linguistic markers from LLM models and incorporates them into supervised AD detection models, while evaluating their model performance and interpretability. Our work consists of the following novelties: First, we design in-context few-shot and zero-shot prompting strategies to facilitate LLMs in extracting high-level linguistic markers discriminative of AD and NC, providing interpretation and assessment of their strength, reliability and relevance to AD classification. Second, we incorporate linguistic markers extracted by LLMs into a smaller AI-driven model to enhance the performance of downstream supervised learning for AD classification, by assigning higher weights to the high-level linguistic markers/features extracted from LLMs. Third, we investigate whether the linguistic markers extracted by LLMs can enhance theaccuracy and interpretability of the downstream supervised learning-based models for AD detection. Our findings suggest that the accuracy of the LLM-extracted linguistic markers-led supervised learning model is less desirable as compared to their counterparts that do not incorporate LLM-extracted markers, highlighting the tradeoffs between interpretability and accuracy in supervised AD classification. Although the use of these interpretable markers may not immediately lead to improved detection accuracy, they significantly improve medical diagnosis and trustworthiness. These interpretable markers allow healthcare professionals to gain a deeper understanding of the linguistic changes that occur in individuals with AD, enabling them to make more informed decisions and provide better patient care.