Work

Dementia Detection by In-Text Pause Encoding

Year: 2024

Type: conference-paper

Abstract: In dementia, particularly Alzheimer's Disease (AD), communication challenges are evident, especially in vocabulary and pragmatic aspects. Affected individuals often use vague, non-specific words, and their speech lacks informative nouns and verbs, leading to imprecise communication. However, aspects like sentence structure, phonology, and articulation are believed to remain intact until later stages, though this view is debated in the research community. The rise of Large Language Models (LLMs) has made significant strides in various domains, including sentiment analysis and question-answering. These advancements have been applied to dementia research, with studies using LLMs to analyze textual data. Some research incorporates pauses in text to enhance performance, while others utilize transfer learning techniques. However, limited datasets for dementia detection pose challenges in training LLMs. Our research presents a novel approach to measuring the impact of in-text encoding strategies by embedding special characters within the text to enhance model performance and incorporating sequences and summaries of their frequency. Our best model achieves 0.88 and 0.86 in f1-score and accuracy, respectively, whereas the baseline has 0.42 and 0.56 in f1-score and accuracy.

Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

DOI: 10.1109/embc53108.2024.10781756

Authors: Reza Soleimani, Shengjie Guo, Katarina L. Haley, Adam Jacks, Edgar Lobaton

Institutions: North Carolina State University, University of North Carolina at Chapel Hill

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Science Foundation