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Nature Communications | Vol.11, Issue.1 | | Pages

Nature Communications

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

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

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.

Original Text (This is the original text for your reference.)

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

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.

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Zhang, Nuofu, Huang, Junzhou, Guan, Weijie, Zhong, Nanshan, Zanin, Mark, Xu, Yuanda, Zhou, Rong, Sang, Ling, Ou, Limin, Huan, Wenjing, Chen, Hanbo, He, Jianxing, Li, Yimin, Zhao, Yi, Chen, Zisheng, Wang, Wei, Lu, Jiatao, Yang, Fan, Wong, SookSan, Guo, Haiyan, Chen, Guoqiang, Liu, Jun, Guo, Jun, Yao, Jianhua, Han, Xiao, Tang, Weimin, Lv, Qingquan, Liang, Hengrui, Chen, Ailan, Lu, Ligong,Liang, Wenhua, Li, Shiyue, Zhou, Niyun,.Early triage of critically ill COVID-19 patients using deep learning. 11 (1),.

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