Shakeri Arezo, Farmanbar Mina
Department of Electrical Engineering and Computer Science Faculty of Science and Technology University of Stavanger Stavanger Norway.
Alzheimers Dement (Amst). 2025 Feb 11;17(1):e70082. doi: 10.1002/dad2.70082. eCollection 2025 Jan-Mar.
Alzheimer's disease (AD) prevalence is increasing, with no current cure. Natural language processing (NLP) offers the potential for non-invasive diagnostics, social burden assessment, and research advancements in AD.
A systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines explored NLP applications in AD, focusing on dataset types, sources, research foci, methods, and effectiveness. Searches were conducted across six databases (ACM, Embase, IEEE, PubMed, Scopus, and Web of Science) from January 2020 to July 2024.
Of 1740 records, 79 studies were selected. Frequently used datasets included speech and electronic health records (EHR), along with social media and scientific publications. Machine learning and neural networks were primarily applied to speech, EHR, and social media data, while rule-based methods were used to analyze literature datasets.
NLP has proven effective in various aspects of AD research, including diagnosis, monitoring, social burden assessment, biomarker analysis, and research. However, there are opportunities for improvement in dataset diversity, model interpretability, multilingual capabilities, and addressing ethical concerns.
This review systematically analyzed 79 studies from six major databases, focusing on the advancements and applications of natural language processing (NLP) in Alzheimer's disease (AD) research.The study highlights the need for models focusing on remote monitoring of AD patients using speech analysis, offering a cost-effective alternative to traditional methods such as brain imaging and aiding clinicians in both prediagnosis and post-diagnosis periods.The use of pretrained multilingual models is recommended to improve AD detection across different languages by leveraging diverse speech features and utilizing publicly available datasets.
阿尔茨海默病（AD）的患病率正在上升，目前尚无治愈方法。自然语言处理（NLP）为AD的非侵入性诊断、社会负担评估和研究进展提供了潜力。
使用系统评价和Meta分析的首选报告项目指南进行系统评价，探讨NLP在AD中的应用，重点关注数据集类型、来源、研究重点、方法和有效性。于2020年1月至2024年7月在六个数据库（ACM、Embase、IEEE、PubMed、Scopus和Web of Science）中进行检索。
在1740条记录中，选取了79项研究。常用的数据集包括语音和电子健康记录（EHR），以及社交媒体和科学出版物。机器学习和神经网络主要应用于语音、EHR和社交媒体数据，而基于规则的方法则用于分析文献数据集。
NLP已在AD研究的各个方面证明有效，包括诊断、监测、社会负担评估、生物标志物分析和研究。然而，在数据集多样性、模型可解释性、多语言能力以及解决伦理问题方面仍有改进的空间。
本综述系统分析了来自六个主要数据库的79项研究，重点关注自然语言处理（NLP）在阿尔茨海默病（AD）研究中的进展和应用。该研究强调需要开发专注于使用语音分析对AD患者进行远程监测的模型，这为脑成像等传统方法提供了一种经济高效的替代方案，并在诊断前和诊断后阶段帮助临床医生。建议使用预训练的多语言模型，通过利用不同的语音特征和公开可用的数据集来提高跨不同语言的AD检测能力。
