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- Title
Early triage of critically ill COVID-19 patients using deep learning.
- Authors
Liang, Wenhua; Yao, Jianhua; Chen, Ailan; Lv, Qingquan; Zanin, Mark; Liu, Jun; Wong, SookSan; Li, Yimin; Lu, Jiatao; Liang, Hengrui; Chen, Guoqiang; Guo, Haiyan; Guo, Jun; Zhou, Rong; Ou, Limin; Zhou, Niyun; Chen, Hanbo; Yang, Fan; Han, Xiao; Huan, Wenjing
- 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. The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern and early assessment would be vital. Here, the authors 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.
- Subjects
GUANGDONG Sheng (China); WUHAN (China); DEEP learning; COVID-19; SARS-CoV-2; CRITICALLY ill; HOSPITAL admission &; discharge
- Publication
Nature Communications, 2020, Vol 11, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-020-17280-8