Nataliia Casnochova Zozuk, Dasa Munkova, Livia Kelebercova, Michal Munk
{"title":"通过NLP提取的语言特征与斯洛伐克临床诊断的阿尔茨海默病和轻度认知障碍的关系","authors":"Nataliia Casnochova Zozuk, Dasa Munkova, Livia Kelebercova, Michal Munk","doi":"10.1002/dad2.70122","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Dementia, particularly Alzheimer's disease (AD), affects language, especially lexical-semantic processing. Discourse analysis using NLP methods can aid early detection, but research in inflectional languages like Slovak is limited.</p><p><strong>Methods: </strong>Speech samples from 216 Slovak-speaking participants (64 AD, 44 MCI, 108 HC) were collected using a picture description task and analyzed for lexical complexity using 15 NLP-based measures.</p><p><strong>Results: </strong>Several lexical complexity measures, including GTTR, UBER, SICHEL, MTLD, HDD and others, significantly differentiated AD or MCI from healthy controls. Some measures (UBER, YULEI, HONORE) also distinguished between AD and MCI.</p><p><strong>Conclusion: </strong>Lexical complexity metrics can serve as non-invasive linguistic indicators of neurodegenerative diseases, demonstrating diagnostic relevance for early detection of AD and MCI in Slovak.</p><p><strong>Highlights: </strong>Lexical complexity metrics effectively differentiate between healthy controls, MCI, and AD in Slovak speakers.Measures such as GTTR, UBER, and HONOR exhibit strong diagnostic potential for neurodegenerative diseases.Education significantly influences linguistic deficits, with higher education correlating to reduced cognitive decline.Findings underscore the importance of studying minority languages for advancing AD and MCI diagnostics.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"17 2","pages":"e70122"},"PeriodicalIF":4.0000,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089133/pdf/","citationCount":"0","resultStr":"{\"title\":\"Relationship between language features extracted through NLP and clinically diagnosed Alzheimer's disease and mild cognitive impairment in Slovak.\",\"authors\":\"Nataliia Casnochova Zozuk, Dasa Munkova, Livia Kelebercova, Michal Munk\",\"doi\":\"10.1002/dad2.70122\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Dementia, particularly Alzheimer's disease (AD), affects language, especially lexical-semantic processing. Discourse analysis using NLP methods can aid early detection, but research in inflectional languages like Slovak is limited.</p><p><strong>Methods: </strong>Speech samples from 216 Slovak-speaking participants (64 AD, 44 MCI, 108 HC) were collected using a picture description task and analyzed for lexical complexity using 15 NLP-based measures.</p><p><strong>Results: </strong>Several lexical complexity measures, including GTTR, UBER, SICHEL, MTLD, HDD and others, significantly differentiated AD or MCI from healthy controls. Some measures (UBER, YULEI, HONORE) also distinguished between AD and MCI.</p><p><strong>Conclusion: </strong>Lexical complexity metrics can serve as non-invasive linguistic indicators of neurodegenerative diseases, demonstrating diagnostic relevance for early detection of AD and MCI in Slovak.</p><p><strong>Highlights: </strong>Lexical complexity metrics effectively differentiate between healthy controls, MCI, and AD in Slovak speakers.Measures such as GTTR, UBER, and HONOR exhibit strong diagnostic potential for neurodegenerative diseases.Education significantly influences linguistic deficits, with higher education correlating to reduced cognitive decline.Findings underscore the importance of studying minority languages for advancing AD and MCI diagnostics.</p>\",\"PeriodicalId\":53226,\"journal\":{\"name\":\"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring\",\"volume\":\"17 2\",\"pages\":\"e70122\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2025-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089133/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/dad2.70122\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/4/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/dad2.70122","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Relationship between language features extracted through NLP and clinically diagnosed Alzheimer's disease and mild cognitive impairment in Slovak.
Background: Dementia, particularly Alzheimer's disease (AD), affects language, especially lexical-semantic processing. Discourse analysis using NLP methods can aid early detection, but research in inflectional languages like Slovak is limited.
Methods: Speech samples from 216 Slovak-speaking participants (64 AD, 44 MCI, 108 HC) were collected using a picture description task and analyzed for lexical complexity using 15 NLP-based measures.
Results: Several lexical complexity measures, including GTTR, UBER, SICHEL, MTLD, HDD and others, significantly differentiated AD or MCI from healthy controls. Some measures (UBER, YULEI, HONORE) also distinguished between AD and MCI.
Conclusion: Lexical complexity metrics can serve as non-invasive linguistic indicators of neurodegenerative diseases, demonstrating diagnostic relevance for early detection of AD and MCI in Slovak.
Highlights: Lexical complexity metrics effectively differentiate between healthy controls, MCI, and AD in Slovak speakers.Measures such as GTTR, UBER, and HONOR exhibit strong diagnostic potential for neurodegenerative diseases.Education significantly influences linguistic deficits, with higher education correlating to reduced cognitive decline.Findings underscore the importance of studying minority languages for advancing AD and MCI diagnostics.
期刊介绍：
Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.
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