Early triage of critically ill COVID-19 patients using deep learning
Early triage of critically ill COVID-19 patients using deep learning
复制标题
使用深度学习对重症 COVID-19 患者进行早期分诊
DOI：
10.1038/s41467-020-17280-8
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发表时间：
2020-07-15
影响因子：
16.6
通讯作者：
He, Jianxing
中科院分区：
文献类型：
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作者：
Liang, Wenhua;Yao, Jianhua;He, Jianxing
2019新型冠状病毒病（COVID-19）患者突然恶化为危重病是一个重大问题。必须及早发现这些患者。我们发现，基于深度学习的生存模型可以根据入院时的临床特征预测COVID-19患者发生危重疾病的风险。我们使用来自575个医疗中心的1590名患者的队列建立了该模型，内部一致性指数为0.894。我们进一步使用来自武汉、湖北和广东省的1393名患者的三个独立队列验证该模型，一致性指数分别为0.890、0.852和0.967。该模型用于创建一个在线计算工具，用于在入院时对患者进行分诊，以识别存在严重疾病风险的患者，确保存在最大严重疾病风险的患者尽早获得适当的护理，并允许有效分配卫生资源。
The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.