通过从电子健康记录数据中学习分布式表示来表征长期COVID时间子类型的表征:一个队列研究
Arianna Dagliati1, Zachary H Strasser2, Zahra Shakeri Hossein Abad3
1Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
EClinicalMedicine
|September 25, 2023
概括
后急性后续COVID (PASC) 的特征是具有挑战性的. 这项研究开发了一个可扩展的框架来识别PASC亚表型,发现15.7%的住院患者出现了症状,关节疼痛和呼吸障碍是最常见的.
科学领域:
- 传染性疾病 传染性疾病
- 数据科学数据科学数据科学
- 公共卫生 公共卫生
背景情况:
- 鉴定COVID (SARS-CoV-2感染) 后急性后遗症或PASC的特征,由于不同的亚表型和定义,提出了挑战.
- 需要可扩展的方法来改善PASC查,管理和治疗计划.
研究的目的:
- 开发和验证一个可扩展的框架,以使用纵向电子健康记录 (EHR) 数据来表征PASC亚现型.
- 增加PASC亚现象定义,并估计其在多个医疗保健系统中的流行率和时间属性.
主要方法:
- 追溯式,多中心观察队列研究,利用来自30422名COVID-19患者的EHR数据.
- 在12424名患者和随访数据上应用了演方法.
- 开发一个分布式表示学习过程,用于PASC亚现象型的定义.
主要成果:
- 该框架确定了七种不同的PASC亚现象.
- 估计15.7%的住院COVID-19患者至少出现了一种PASC症状,5.98%出现多种症状.
- 关节疼痛 (5.45%) 和呼吸障碍 (4.53%) 是PASC最常见的症状.
结论:
- 建立了一个可扩展的,统一的框架,用于在医疗保健系统中估计PASC亚现象的流行率和时间特征.
- 这种方法有助于加强PASC查,管理和治疗规划.
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