比较自然语言处理和结构化医疗数据,为因COVID-19住院的患者开发可计算的表型:回顾性分析
Feier Chang1, Jay Krishnan2, Jillian H Hurst3
1Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States.
JMIR medical informatics
|August 25, 2023
概括
确定住院的主要原因对于准确的COVID-19指标至关重要. 使用临床笔记的可计算表型有效地区分COVID-19住院和偶然的阳性测试.
科学领域:
- 医疗信息学 医疗信息学
- 临床流行病学临床流行病学
- 自然语言处理自然语言处理.
背景情况:
- 医院经常测试COVID-19患者,包括因其他原因入院的人.
- 准确识别COVID-19住院情况对于公共卫生指标至关重要.
研究的目的:
- 比较COVID-19住院治疗的可计算表型定义.
- 通过结构化电子健康记录 (EHR) 数据,临床笔记或两者一起评估表型.
主要方法:
- 对586名住院患者进行了回顾性分析,结果为SARS-CoV-2呈阳性.
- 使用LASSO回归和随机森林与EHR数据和临床笔记 (NLP).
- 使用临床医生图表审查和AUROC指标验证的模型.
主要成果:
- 38.2%的患者因非COVID-19的原因住院.
- 使用临床笔记的表型表现优于结构化的EHR数据 (AUROC 0.894vs0.841).
- 因果特异性表型显著影响了医院结果指标.
结论:
- 为准确的COVID-19住院数据,因果特异性表型是必不可少的.
- 自然语言处理有效地从临床笔记中提取住院治疗的指示.
- 当多种疾病可能表明住院治疗时,这种方法是有价值的.
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