来自MIMIC-III的临床文本的背景化种族和种族注释
Oliver J Bear Don't Walk1, Adrienne Pichon2, Harry Reyes Nieva2,3
1University of Washington, Seattle, Washington, USA. obdw4@uw.edu.
Scientific data
|December 5, 2024
概括
准确的种族和民族 (RE) 数据对于健康公平研究至关重要. C-REACT数据集使用自然语言处理从临床笔记中提取详细的RE信息,提高患者数据的完整性.
科学领域:
- 临床信息学 临床信息学
- 健康 公平 研究 健康 公平 研究
- 自然语言处理自然语言处理.
背景情况:
- 准确的种族和民族 (RE) 数据对于观察性健康研究至关重要,包括队列特征,质量评估和识别健康差异.
- 电子健康记录 (EHR) 通常含有不完整,不准确或不够细化的患者级RE数据.
- 自然语言处理 (NLP) 提供了一种从非结构化临床文本中提取RE信息的方法,以补充现有数据.
研究的目的:
- 介绍临床文本 (C-REACT) 数据集的上下文化种族和种族注释.
- 为改善健康研究中种族和种族数据的完整性和准确性提供一个有价值的资源.
- 支持卫生系统利用可再生能源数据为健康公平倡议提供支持.
主要方法:
- 开发了C-REACT数据集,使用12,000名患者和MIMIC-III临床笔记中的17,281个句子.
- 在临床文本中为RE数据创建了两组参考标准注释.
- 提供了详细的注释指南,以实现一致和可靠的数据标签.
主要成果:
- C-REACT数据集包含细分的RE信息,包括首选语言和原产国.
- 第二组注释包括医生注释的RE标签.
- 该数据集有助于从临床叙述中提取全面的RE数据.
结论:
- C-REACT数据集增强了健康研究中患者级RE数据的实用性.
- 这种资源可以显著帮助卫生系统解决健康差异,促进健康公平.
- 从NLP衍生出的RE数据可以弥合结构化EHR数据中的差距,从而产生更强大的健康分析.
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