Nature Communications (Oct 2020)

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19

  • Yue Gao,
  • Guang-Yao Cai,
  • Wei Fang,
  • Hua-Yi Li,
  • Si-Yuan Wang,
  • Lingxi Chen,
  • Yang Yu,
  • Dan Liu,
  • Sen Xu,
  • Peng-Fei Cui,
  • Shao-Qing Zeng,
  • Xin-Xia Feng,
  • Rui-Di Yu,
  • Ya Wang,
  • Yuan Yuan,
  • Xiao-Fei Jiao,
  • Jian-Hua Chi,
  • Jia-Hao Liu,
  • Ru-Yuan Li,
  • Xu Zheng,
  • Chun-Yan Song,
  • Ning Jin,
  • Wen-Jian Gong,
  • Xing-Yu Liu,
  • Lei Huang,
  • Xun Tian,
  • Lin Li,
  • Hui Xing,
  • Ding Ma,
  • Chun-Rui Li,
  • Fei Ye,
  • Qing-Lei Gao

DOI
https://doi.org/10.1038/s41467-020-18684-2
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 10

Abstract

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Methods to stratify patients according to mortality risk are essential to allocate limited heath resources during the COVID-19 crisis. Here, using machine learning methods, the authors present a mortality risk prediction model for COVID-19 that uses patients’ clinical data on admission to stratify patients by mortality risk.