增强企业数据仓库以使用自然语言处理提取的数据进行研究
Tanja Magoc1, Russell Everson2, Christopher A Harle3
1College of Medicine, University of Florida, Gainesville, FL, USA.
Journal of clinical and translational science
|July 17, 2023
概括
本研究介绍了一种可通用的架构,用于将自然语言处理 (NLP) 数据集成到企业研究数据仓库 (EDW4R). 这提高了研究人员的数据可用性,而不需要NLP专业知识,证明了显著的附加值.
科学领域:
- 生物医学信息学 生物医学信息学
- 健康 数据科学 数据科学
- 临床研究信息学 临床研究信息学
背景情况:
- 企业研究数据仓库 (EDW4R) 往往缺乏临床笔记中的全面数据.
- 自然语言处理 (NLP) 可以从非结构化的临床叙述中提取有价值的离散数据.
- 将NLP衍生数据集成到EDW4R中可以显著提高研究能力.
研究的目的:
- 开发一个可通用的架构,以通过NLP模型输出增强EDW4R.
- 让临床笔记中的离散数据广泛用于研究,而不需要NLP专业知识.
- 量化从EDW4R.临床叙述中提取的信息的附加值.
主要方法:
- 一个现有的NLP模型在一个学术医疗中心使用一个月的临床记录进行了评估.
- 手动审查用于NLP模型性能分析.
- 描述了一个详细的,可复制的架构来增强EDW4R.
主要成果:
- 在两周内,EDW4R被增强了2.5亿份临床笔记的数据.
- 对于两个关键变量,NLP产生了16%和39%的数据可用性增加.
- 一个独立的团队证实了NLP模型的高正预测值.
结论:
- 开发的架构对新的NLP模型具有很高的概括性.
- 在EDW4R中,NLP显著增加了已经以结构化格式存在的数据的价值.
- 通过NLP提取的数据来增强EDW4R,使研究团队缺乏NLP专业知识.
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