RECAP-KG:从原始初级保健医生笔记中挖掘知识图,用于初级保健中远程COVID-19评估
Rachel Lee Mekhtieva1, Brandon Forbes1,2, Dalal Alrajeh1
1Department of Computing, Imperial College London, London, UK.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
我们开发了RECAP-KG,这是一个使用SNOMED-CT从临床笔记中提取结构化知识图的框架. 这种方法比临床决策支持系统的传统NLP方法提高了准确性.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床决策支持 临床决策支持
背景情况:
- 临床决策支持系统需要结构化的临床数据.
- 非结构化的临床笔记对自然语言处理 (NLP) 提出了挑战,原因是错误和缺乏语法.
- 现有的NLP方法难以应对临床笔记的复杂性.
研究的目的:
- 介绍RECAP-KG,这是一个新的框架,用于从初级保健临床笔记中构建知识图.
- 从非结构化的临床文本中提取结构化的知识图.
- 提高临床数据处理NLP方法的准确性.
主要方法:
- 开发了RECAP-KG,一个知识图表构建框架.
- 利用SNOMED-CT本体 (完整的层次结构和COVID相关子集) 来提取知识.
- 将框架应用于英国COVID-19临床评估服务 (CCAS) 数据集.
主要成果:
- 成功地从初级保健临床笔记中提取了结构化知识图.
- 与传统的NLP方法相比,已经证明了定量准确性的改进.
- 展示了框架在回答患者相关问题的有效性.
结论:
- RECAP-KG为处理非结构化临床笔记提供了一个有效的解决方案.
- 该框架提高了临床决策支持系统的准确性.
- 从临床笔记中构建知识图是改善数据利用的可行方法.
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