针对急性健康状况的数据驱动的自动分类算法:将PheNorm应用于COVID-19疾病
Joshua C Smith1, Brian D Williamson2, David J Cronkite2
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Journal of the American Medical Informatics Association : JAMIA
|December 18, 2023
概括
使用PheNorm的自动表型化有效识别了症状性COVID-19,证明了其用于快速急性状况分析的可行性. 这种方法减少了开发时间和跨多个站点的运营商依赖.
科学领域:
- 计算流行病学计算流行病学
- 医疗信息学 医疗信息学
- 临床信息学 临床信息学
背景情况:
- 与手动方法相比,自动化表型算法提供了较短的开发时间和操作员依赖性.
- 虽然PheNorm方法在识别慢性疾病方面取得了成功,但它对急性疾病的实用性仍然在很大程度上未被探索.
研究的目的:
- 实施和评估PheNorm对症状性COVID-19的自动化表型方法.
- 用PheNorm.来研究急性健康状况快速表型化的可行性.
主要方法:
- 使用自然语言处理,机器学习和银标准标签的自动化算法PheNorm应用于两个机构的COVID-19患者队列.
- 使用黄金标准手动图表审查来评估性能,探索特征工程的变化和外部训练模型的使用.
主要成果:
- 在这两个机构,PheNorm模型实现了高性能指标,包括AUC,灵敏度和积极的预测值.
- 模型性能根据银标准标签和特征工程选择而有所不同,当地训练有素的模型在其他地点应用时表现出有效性.
结论:
- 菲诺姆成功识别了症状性COVID-19,一种急性健康状况.
- 与传统的表型化方法相比,PheNorm的方法很简单,可以在多个网站上进行扩展,并显著减少了开销.
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