A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.

Journal: Journal of medical Internet research
Published Date: Apr 7, 2021

Abstract

BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis.

Authors

  • Yuanfang Chen
    Institute of HIV/AIDS/STI Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
  • Liu Ouyang
    Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Forrest S Bao
    Department of Computer Science, Iowa State University, Ames, IA, United States.
  • Qian Li
    Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
  • Lei Han
    Jiangsu Province Center for Disease Prevention and Control, Institute of Occupational Disease Prevention, Nanjing, Jiangsu, China.
  • Hengdong Zhang
    Department of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
  • Baoli Zhu
    School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Yaorong Ge
    Department of Software and Information Systems, University of North Carolina at Charlotte, Charlotte, NC, United States.
  • Patrick Robinson
    Department of Public Health Sciences, College of Health and Human Services, University of North Carolina Charlotte, Charlotte, NC, United States.
  • Ming Xu
    Shenyang Analytical Application Center, Shimadzu (China) Co. Ltd., Shenyang, 167 Qingnian Street, Shenyang, 110016, PR China.
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Shi Chen
    Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, PUMCH, CAMS & PUMC, Beijing, China.

Keywords

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