This paper is in the following e-collection/theme issue:

Statistical Methods for Surveillance and Population Health (148) JMIR Theme Issue: COVID-19 Special Issue (2394) Machine Learning (1400) Innovative Methods in Public Health and Surveillance (307) Artificial Intelligence (1214) Infectious Diseases (non-STD/STI) (1490) Surveillance Systems (430) Public Health Informatics (80) Outbreak and Pandemic Preparedness and Management (1679)

Published on in Vol 7, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29544, first published .
Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach

Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach

Authors of this article:

Kenneth Chi-Yin Wong1 Author Orcid Image ;   Yong Xiang1 Author Orcid Image ;   Liangying Yin1 Author Orcid Image ;   Hon-Cheong So1, 2, 3, 4, 5, 6, 7 Author Orcid Image
Article Authors Cited by (16) Tweetations (4) Metrics

Kenneth Chi-Yin Wong   1 , MSc ;   Yong Xiang   1 , MBBS ;   Liangying Yin   1 , MPhil ;   Hon-Cheong So   1, 2, 3, 4, 5, 6, 7 , MBBS, PhD

1 School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, China

2 KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Kunming, China

3 CUHK Shenzhen Research Institute, Shenzhen, China

4 Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China

5 Margaret K.L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong, Hong Kong, China

6 Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong, China

7 Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong, Hong Kong, China

Corresponding Author:

  • Hon-Cheong So, MBBS, PhD
  • School of Biomedical Sciences, The Chinese University of Hong Kong
  • RM 520A, Lo Kwee Seong Biomedical Sciences Buildiing
  • Chinese University of Hong Kong
  • Hong Kong
  • China
  • Phone: 86 39439255
  • Email: hcso@cuhk.edu.hk