Article
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Dementia Detection by In-Text Pause Encoding
Version 1
: Received: 8 August 2024 / Approved: 9 August 2024 / Online: 9 August 2024 (16:50:52 CEST)
How to cite: Soleimani, R.; Guo, S.; Haley, K.; Jacks, A.; Lobaton, E. Dementia Detection by In-Text Pause Encoding. Preprints 2024, 2024080727. https://doi.org/10.20944/preprints202408.0727.v1 Soleimani, R.; Guo, S.; Haley, K.; Jacks, A.; Lobaton, E. Dementia Detection by In-Text Pause Encoding. Preprints 2024, 2024080727. https://doi.org/10.20944/preprints202408.0727.v1
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.
Keywords
Dementia, Speech Analysis, LLMs, NLP
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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