Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.

Journal: Frontiers in immunology
PMID: 39403385

Abstract

BACKGROUNDS: During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world's healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) algorithms may be able to provide more accurate advice on the classification of COVID-19 patients, offering predictive, preventive, and personalized medicine (PPPM) solutions in the future.

Authors

  • Qiangqiang Qin
    Department of Respiratory Medicine, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Qingxuan Li
    State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China.
  • Guiyin Zhu
    Department of Respiratory Medicine, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Haiyang Yu
    State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
  • Mingyan Peng
    Department of Gynecology and Obstetrics, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Shuang Wu
  • Xue Xu
    Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China; Marcus Institute for Aging Research, Hebrew SeniorLife and Harvard Medical School, Boston, MA, 02131, USA.
  • Wen Gu
    Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China.
  • Xuejun Guo
    Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China. guoxuejun@xinhuamed.com.cn.

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