Early triage of critically ill COVID-19 patients using deep learning


Liang WenhuaORCID,Yao Jianhua,Chen Ailan,Lv Qingquan,Zanin Mark,Liu JunORCID,Wong SookSan,Li YiminORCID,Lu Jiatao,Liang HengruiORCID,Chen Guoqiang,Guo Haiyan,Guo Jun,Zhou Rong,Ou Limin,Zhou Niyun,Chen Hanbo,Yang Fan,Han Xiao,Huan Wenjing,Tang Weimin,Guan Weijie,Chen Zisheng,Zhao Yi,Sang Ling,Xu Yuanda,Wang Wei,Li Shiyue,Lu Ligong,Zhang Nuofu,Zhong Nanshan,Huang Junzhou,He JianxingORCID


AbstractThe 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.


Springer Science and Business Media LLC


General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry








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