在电子健康记录中提取补充和整合性健康方法
Huixue Zhou1, Greg Silverman2, Zhongran Niu1
1Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55414 USA.
Journal of healthcare informatics research
|August 28, 2023
概括
电子健康记录 (EHR) 可以捕捉像音乐治疗这样的补充和整合性健康 (CIH) 方法. 自然语言处理 (NLP) 系统显示出潜力,但需要改进,以便在临床笔记中识别CIH数据.
科学领域:
- 综合医学是一个整体的医学.
- 医疗信息学 医疗信息学
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 补充和整合性健康 (CIH) 的使用正在增加,但对影响和不良事件的真实数据有限.
- 电子健康记录 (EHR) 为这些数据提供了一个潜在的来源,但捕获CIH信息是具有挑战性的.
- 目前在电子健康记录中识别CIH的现有方法尚未完全理解.
研究的目的:
- 在EHR系统中代表心理和身体CIH方法 (音乐治疗,手术,水上运动).
- 评估当前自然语言处理 (NLP) 系统在从临床笔记中识别CIH方法方面的有效性.
- 建立在CIH临床研究中利用EHR数据的基础.
主要方法:
- 手动注释了300份关于CIH状态,症状和频率的临床笔记,以创建一个黄金标准数据集.
- 对CIH概念提取的黄金标准进行了评估,对三个NLP系统 (BioMedICUS,MetaMap,cTAKES) 进行了评估.
- 使用Venn图来比较通过当前程序术语 (CPT) 代码的EHR数据检索与SQL中的关键字搜索.
主要成果:
- 在临床笔记中,CPT代码与CIH方法的重叠是有限的,与关键字搜索不同.
- 评估的NLP系统在三种CIH方法中实现了0.41的平均宽容匹配F1得分.
- 在水上运动中,BioMedICUS的表现最高,F1得分为0.66.
结论:
- 在临床笔记中代表CIH对于利用EHR数据至关重要.
- 目前的NLP系统在提取CIH信息方面表现适度,这表明有改进的余地.
- 这项研究为未来使用电子健康记录的CIH研究提供了一个框架.
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