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

Author

Listed:
  • Wenhua Liang

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Jianhua Yao

    (Tencent AI Lab)

  • Ailan Chen

    (The First Affiliated Hospital of Guangzhou Medical University
    Hankou Hospital)

  • Qingquan Lv

    (Hankou Hospital)

  • Mark Zanin

    (The University of Hong Kong)

  • Jun Liu

    (The First Affiliated Hospital of Guangzhou Medical University
    The First Affiliated Hospital of Guangzhou Medical University)

  • SookSan Wong

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Yimin Li

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Jiatao Lu

    (Hankou Hospital)

  • Hengrui Liang

    (The First Affiliated Hospital of Guangzhou Medical University
    The First Affiliated Hospital of Guangzhou Medical University)

  • Guoqiang Chen

    (Foshan Hospital)

  • Haiyan Guo

    (Foshan Hospital)

  • Jun Guo

    (Daye Hospital)

  • Rong Zhou

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Limin Ou

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Niyun Zhou

    (Tencent AI Lab)

  • Hanbo Chen

    (Tencent AI Lab)

  • Fan Yang

    (Tencent AI Lab)

  • Xiao Han

    (Tencent AI Lab)

  • Wenjing Huan

    (Tencent Healthcare)

  • Weimin Tang

    (Tencent Healthcare)

  • Weijie Guan

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Zisheng Chen

    (The First Affiliated Hospital of Guangzhou Medical University
    The Sixth Affiliated Hospital of Guangzhou Medical University)

  • Yi Zhao

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Ling Sang

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Yuanda Xu

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Wei Wang

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Shiyue Li

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Ligong Lu

    (Zhuhai People Hospital)

  • Nuofu Zhang

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Nanshan Zhong

    (The First Affiliated Hospital of Guangzhou Medical University)

  • Junzhou Huang

    (Tencent AI Lab)

  • Jianxing He

    (The First Affiliated Hospital of Guangzhou Medical University)

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.

Suggested Citation

  • Wenhua Liang & Jianhua Yao & Ailan Chen & Qingquan Lv & Mark Zanin & Jun Liu & SookSan Wong & Yimin Li & Jiatao Lu & Hengrui Liang & Guoqiang Chen & Haiyan Guo & Jun Guo & Rong Zhou & Limin Ou & Niyun, 2020. "Early triage of critically ill COVID-19 patients using deep learning," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17280-8
    DOI: 10.1038/s41467-020-17280-8
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    References listed on IDEAS

    as
    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
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    Cited by:

    1. Longling Zhang & Bochen Shen & Ahmed Barnawi & Shan Xi & Neeraj Kumar & Yi Wu, 2021. "FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia," Information Systems Frontiers, Springer, vol. 23(6), pages 1403-1415, December.
    2. Francesco Piccialli & Vincenzo Schiano Cola & Fabio Giampaolo & Salvatore Cuomo, 2021. "The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 23(6), pages 1467-1497, December.

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