Knowledge-Based Model for Detecting Neurodegenerative Diseases Using Text Complexity Measures
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17250%2F25%3AA2603AGI" target="_blank" >RIV/61988987:17250/25:A2603AGI - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/10.1007/978-3-031-83207-9_26" target="_blank" >https://link.springer.com/10.1007/978-3-031-83207-9_26</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-83207-9_26" target="_blank" >10.1007/978-3-031-83207-9_26</a>
Alternative languages
Result language
angličtina
Original language name
Knowledge-Based Model for Detecting Neurodegenerative Diseases Using Text Complexity Measures
Original language description
The incidence of neurodegenerative diseases affecting the brain and its cognitive functions, including language and speech, is increasing in society. These diseases impact the manner and quality of speech and can be detected through non-invasive methods. Understanding language involves analyzing internal linguistic features such as text readability and complexity. Language complexity is a significant measure of an individual’s linguistic development, representing an independent dimension of utterance (whether written or spoken) and manifesting across all linguistic levels (phonological, morphological, syntactic, and semantic). The aim of this research is to identify linguistic features – measures of text complexity – that may serve as predictors for a knowledge-based model to detect neurodegenerative diseases such as Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and Parkinson’s Disease (PD) in the context of the inflectional Slovak language. The results indicate that lexical measures of language complexity that are independent of text length are unsuitable for predicting neurodegenerative diseases such as AD/MCI or PD. However, they can be useful in distinguishing between AD/MCI and PD. The rate of action in describing a situational picture is a strong predictor for distinguishing AD/MCI but not PD. The sequence of two verbs is a strong predictor for diagnosing both AD/MCI and PD, but does not distinguish between these diseases. Last but not least, vocabulary range and diversity influence not only the diagnosis of neurodegenerative diseases, but also help differentiate between AD and PD.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
60203 - Linguistics
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2024
ISBN
978-3-031-83207-9
Number of pages of the result
13
Pages from-to
368-380
Number of pages of the book
462
Publisher name
Springer
Place of publication
Cham
UT code for WoS chapter
001472382700026