将自然语言处理应用于临床数据仓库中的文本数据:系统审查
Adrien Bazoge1,2, Emmanuel Morin1, Béatrice Daille1
1Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France.
本综述探讨了自然语言处理 (NLP) 在临床数据仓库 (CDW) 中如何构建有价值的信息. 通过提取和转换数据,NLP增强了临床研究,尽管仍然存在挑战,特别是非英语文本.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算语言学 计算语言学
- 健康 数据科学 数据科学
背景情况:
- 临床数据仓库 (CDW) 允许从临床护理中二次使用健康数据.
- 非结构化的临床文本包含有意义的,高价值的信息.
- 自然语言处理 (NLP) 可以对这些文本数据进行结构化并提高可访问性.
研究的目的:
- 审查将NLP应用于CDWs文本数据的研究.
- 为了确定在CDW数据上执行的常见NLP任务.
- 为这些任务使用的NLP方法进行分类.
主要方法:
- 按照PRISMA指南进行系统审查.
- 在PubMed,Google Scholar和ACL Anthology数据库中进行了搜索.
- 包括1995-2021年的英语文章,重点关注NLP和CDW文本数据.
主要成果:
- 在1353篇文章中,有194篇符合纳入标准.
- 信息提取 (57.7%) 和患者识别 (26.3%) 是NLP的主要任务.
- 象征性方法 (53.4%) 是最常见的,其次是机器学习 (30.2%) 和深度学习 (16.4%).
- 大多数NLP应用程序专注于英语语言数据 (78.9%).
结论:
- CDW对于研究中临床文本的二次使用至关重要.
- 临床NLP有效地访问,提取和转换CDW数据.
- 挑战仍然存在,特别是对于非英语语言,但NLP影响临床研究和实践.
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