Relationship between language features extracted through NLP and clinically diagnosed Alzheimer's disease and mild cognitive impairment in Slovak.

IF 4 Q1 CLINICAL NEUROLOGY

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Pub Date : 2025-05-19 eCollection Date: 2025-04-01 DOI:10.1002/dad2.70122

Nataliia Casnochova Zozuk, Dasa Munkova, Livia Kelebercova, Michal Munk

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引用次数: 0

Abstract

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.

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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

背景：痴呆症，特别是阿尔茨海默病（AD），影响语言，特别是词汇-语义加工。使用NLP方法的话语分析可以帮助早期发现，但对斯洛伐克语等屈折语言的研究有限。方法：使用图片描述任务收集216名斯洛伐克语参与者（AD 64人，MCI 44人，HC 108人）的语音样本，并使用15种基于nlp的测量方法分析词汇复杂性。结果：GTTR、UBER、SICHEL、MTLD、HDD等词汇复杂度指标可显著区分AD或MCI与健康对照。一些指标（UBER、YULEI、HONORE）也区分了AD和MCI。结论：词汇复杂性指标可以作为神经退行性疾病的非侵入性语言指标，证明了斯洛伐克AD和MCI早期检测的诊断相关性。重点：词汇复杂性指标可以有效区分斯洛伐克语使用者的健康对照、MCI和AD。GTTR、UBER和HONOR等指标显示出对神经退行性疾病的强大诊断潜力。教育显著影响语言缺陷，高等教育与认知能力下降的减少相关。研究结果强调了研究少数民族语言对推进AD和MCI诊断的重要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring Medicine-Psychiatry and Mental Health

CiteScore

7.80

自引率

7.50%

发文量

101

审稿时长

8 weeks

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.