Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection

TL;DR

This paper introduces a novel framework that uses large language model reasoning to profile patient transcripts, improving Alzheimer's detection accuracy and interpretability by capturing linguistic deficits.

Contribution

It presents a patient-level transcript profiling method leveraging LLM reasoning augmentation, enhancing AD detection and interpretability over existing transcript-based approaches.

Findings

Achieved 8.51% accuracy improvement on ADReSS dataset

Identified meaningful linguistic deficit attributes

Demonstrated potential for LLM-based AD detection interpretation

Abstract

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.