Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease
This work addresses the problem of scalable and interpretable Alzheimer's disease detection for aging populations, though it appears incremental as it builds on existing methods like LLMs and TF-IDF.
The researchers tackled Alzheimer's disease screening by developing a compact and explainable feature set from spoken language data, which outperformed traditional linguistic features across two classifiers with high dimension efficiency.
Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society. The use of spoken language-based AD detection methods has gained prevalence due to their scalability due to their scalability. Based on the Cookie Theft picture description task, we devised an explainable and effective feature set that leverages the visual capabilities of a large language model (LLM) and the Term Frequency-Inverse Document Frequency (TF-IDF) model. Our experimental results show that the newly proposed features consistently outperform traditional linguistic features across two different classifiers with high dimension efficiency. Our new features can be well explained and interpreted step by step which enhance the interpretability of automatic AD screening.