基于EHR的临床研究的开放自然语言处理 (NLP) 框架:使用国家COVID队列协作 (N3C) 的案例演示
Sijia Liu1, Andrew Wen1, Liwei Wang1
1Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA.
概括
开发临床自然语言处理 (NLP) 模型用于2019年冠状病毒病 (COVID-19) 症状和体征是具有挑战性的,因为多个站点的数据变化. 多站点数据和联合方法对于在翻译研究中强大的NLP模型开发至关重要.
科学领域:
- 临床自然语言处理 (NLP)
- 翻译研究是翻译研究.
- 在COVID-19研究研究中.
背景情况:
- 临床NLP模型的采用受到过程异质性和人为因素的限制.
- 开发强大的NLP模型需要多个站点的数据来实现概括性.
- 翻译研究面临着跨机构数据和流程标准化的挑战.
研究的目的:
- 报告开发NLP解决方案的新冠肺炎病 2019 (COVID-19) 征兆和症状提取.
- 突出在NLP模型开发中使用多站点数据的好处.
- 强调在多个站点的NLP项目中需要联合注释和评估.
主要方法:
- 在一个开放的NLP框架内开发了一个NLP解决方案.
- 利用了国家COVID队列 (N3C) 的参与站点的数据子集.
- 在NLP模型开发中使用符号和统计方法.
主要成果:
- 证明了多站点数据对符号和统计NLP方法的实证益处.
- 确定了在多个站点设置中的开发过程中遇到的几个陷.
- 展示了一个开放的NLP框架对协作研究的实用性.
结论:
- 多站点数据显著有利于临床NLP模型的稳定性和通用性.
- 联合注释和评估策略对于克服多站点NLP开发的挑战至关重要.
- 这些发现支持使用开放的框架和协作方法来推进临床NLP的翻译研究.
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