阿尔茨海默病研究中的自然语言处理:方法,数据和疗效的系统审查
Arezo Shakeri1, Mina Farmanbar1
1Department of Electrical Engineering and Computer Science Faculty of Science and Technology University of Stavanger Stavanger Norway.
Alzheimer's & dementia (Amsterdam, Netherlands)
|February 12, 2025
概括
自然语言处理 (NLP) 对阿尔茨海默病 (AD) 研究有前景,有助于诊断和监测. 需要进一步开发各种数据集和多语言应用程序,以提高AD检测和患者护理.
科学领域:
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
- 神经科学是一个神经科学.
背景情况:
- 全球阿尔茨海默病 (AD) 患病率正在上升,这对公众健康构成了重大挑战.
- 目前对AD的诊断和监测方法可能是侵入性的和昂贵的.
- 自然语言处理 (NLP) 为非侵入性AD评估和研究提供了一个潜在的途径.
研究的目的:
- 系统地审查NLP在阿尔茨海默病研究中的应用.
- 确定共同的数据集,研究重点,方法和NLP技术在AD中的有效性.
- 突出未来开发和改进NLP用于AD的领域.
主要方法:
- 系统性审查遵循系统性审查和元分析 (PRISMA) 准则的首选报告项目.
- 从2020年1月到2024年7月,在六个主要的学术数据库 (ACM,Embase,IEEE,PubMed,Scopus,Web of Science) 进行了搜索.
- 分析了79项从1740个记录的初始池中选择的研究,重点是NLP在AD中的应用.
主要成果:
- 语音和电子健康记录 (EHR) 是最常用的数据集.
- 机器学习和神经网络主要应用于语音,电子健康记录和社交媒体数据.
- 基于规则的方法主要用于分析科学文献数据集.
结论:
- 在AD研究的各个方面,NLP是有效的,包括诊断,监测,社会负担评估和生物标志物分析.
- 有机会增强数据集多样性,模型解释性和多语言能力在NLP的AD.
- 语音分析为阿尔茨海默病患者提供了一种具有成本效益的远程监测解决方案,有助于诊断前后的临床决策.
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