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Natural language processing-driven framework for the early detection of language and cognitive decline
Perez Cabello de Alba, M.B.
Perez Cabello de Alba, M.B.
Publication Date
2023-12
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(c) 2023 The Authors. This is an Open Access article distributed under the Creative Commons CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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openAccess
Accepted for publication
2023-09-20
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Abstract
Natural Language Processing (NLP) technology has the potential to provide a non-invasive, cost-effective method using a timely intervention for detecting early-stage language and cognitive decline in individuals concerned about their memory. The proposed pre-screening language and cognition assessment model (PST-LCAM) is based on the functional linguistic model Role and Reference Grammar (RRG) to analyse and represent the structure and meaning of utterances, via a set of language production and cognition parameters. The model is trained on a DementiaBank dataset with markers of cognitive decline aligned to the global deterioration scale (GDS). A hybrid approach of qualitative linguistic analysis and assessment is applied, which includes the mapping of participants´ tasks of speech utterances and words to RRG phenomena. It uses a metric-based scoring with resulting quantitative scores and qualitative indicators as pre-screening results. This model is to be deployed in a user-centred conversational assessment platform.
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Citation
Panesar K and Pérez Cabello de Alba MB (2023) Natural language processing-driven framework for the early detection of language and cognitive decline. Journal of Language and Health. 1(2): 20-35.
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Article