一个可计算的病例定义,用于在医院外发生的SARS-CoV2测试患者
Lijing Wang1, Amy R Zipursky2, Alon Geva3
1Department of Data Science, New Jersey Institute of Technology, Newark, New Jersey, USA.
JAMIA open
|July 10, 2023
概括
这项研究开发了一种机器学习模型,使用电子健康记录 (EHR) 中的临床笔记来检测COVID-19病例. 该模型可靠地识别了病例,即使实验室数据缺失,也改善了COVID-19队列识别.
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.
- 流行病学 流行病学
背景情况:
- 电子健康记录 (EHR) 包含有价值的临床信息.
- 识别COVID-19病例通常依赖于结构化的实验室数据.
- 电子健康记录中的非结构化临床文本可以提供关键的诊断信息.
研究的目的:
- 开发一种方法来识别COVID-19病例,只使用电子健康记录中的非结构化文本.
- 为了捕获结构化实验室数据遗漏的COVID-19病例.
- 从临床笔记建立一个可靠的SARS-CoV-2检测分类器.
主要方法:
- 训练有素的统计分类器从非结构化的EHR文本中对特征表示进行训练.
- 为了培训,利用了经过COVID-19 PCR测试确认的患者的代理数据集.
- 通过单独的数据集和专家医生审查验证了分类器的性能.
主要成果:
- 最好的分类器在代理数据集上的SARS-CoV-2阳性病例中获得了0.56 F1分数,0.6精度和0.52回忆.
- 专家验证显示了高准确度:97.6%的COVID-19阳性和97.8%的阴性病例.
- 该模型确定了结构化实验室数据未捕捉到的额外病例.
结论:
- 机器学习分类器可以从EHR临床文本中可靠地检测COVID-19病例.
- 对代理数据集的培训是开发高性能分类器的有效策略.
- 这种方法提高了识别综合COVID-19队伍的能力.
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