Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

Journal: International journal of epidemiology
Published Date:

Abstract

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early.

Authors

  • Chuanyu Hu
    Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Zhenqiu Liu
    1 Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center , Los Angeles, California.
  • Yanfeng Jiang
    State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China.
  • Oumin Shi
    Health Science Center, Shenzhen Second People's Hospital, TFirst Affiliated Hospital of Shenzhen University, Shenzhen, China.
  • Xin Zhang
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Kelin Xu
    Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.
  • Chen Suo
    Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
  • Qin Wang
    Department of Pharmacy, Affiliated Hospital of Nantong University, Nantong, China.
  • Yujing Song
    Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Kangkang Yu
    Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China.
  • Xianhua Mao
    State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China.
  • Xuefu Wu
    Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
  • Mingshan Wu
    Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
  • Tingting Shi
    Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
  • Wei Jiang
    Department of Civil Engineering, Johns Hopkins System Institute, Johns Hopkins University, Baltimore, Maryland.
  • Lina Mu
    Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo State University of New York, Buffalo, NY, USA.
  • Damien C Tully
    Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
  • Li Jin
    State Key Laboratory of Genetic Engineering and Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China.
  • Shusheng Li
    Department of Computer Science, Xiamen University, Xiamen 361005, China.
  • Xuejin Tao
    Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Tiejun Zhang
    Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.
  • Xingdong Chen
    State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China.