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 (1401) Innovative Methods in Public Health and Surveillance (307) Artificial Intelligence (1216) 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

Journals

  1. Leni R, Belladelli F, Baldini S, Scroppo F, Zaffuto E, Antonini G, Montorsi F, Salonia A, Carcano G, Capogrosso P, Dehò F. The Complex Interplay between Serum Testosterone and the Clinical Course of Coronavirus Disease 19 Pandemic: A Systematic Review of Clinical and Preclinical Evidence. The World Journal of Men's Health 2023;41(3):466 View
  2. Wan E, Mathur S, Zhang R, Yan V, Lai F, Chui C, Li X, Wong C, Chan E, Yiu K, Wong I. Association of COVID-19 with short- and long-term risk of cardiovascular disease and mortality: a prospective cohort in UK Biobank. Cardiovascular Research 2023;119(8):1718 View
  3. Buttia C, Llanaj E, Raeisi-Dehkordi H, Kastrati L, Amiri M, Meçani R, Taneri P, Ochoa S, Raguindin P, Wehrli F, Khatami F, Espínola O, Rojas L, de Mortanges A, Macharia-Nimietz E, Alijla F, Minder B, Leichtle A, Lüthi N, Ehrhard S, Que Y, Fernandes L, Hautz W, Muka T. Prognostic models in COVID-19 infection that predict severity: a systematic review. European Journal of Epidemiology 2023;38(4):355 View
  4. Azizi Z, Shiba Y, Alipour P, Maleki F, Raparelli V, Norris C, Forghani R, Pilote L, El Emam K. Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data. BMJ Open 2022;12(5):e050450 View
  5. Esen S, Basak C, Leyla Ö, Aslıhan A, Evrim Eylem A. The effect of ACE2 receptor, IFN-γ, and TNF-α polymorphisms on the severity and prognosis of the disease in SARS-CoV-2 infection. Journal of Investigative Medicine 2023;71(5):526 View
  6. Orozco-Beltrán D, Merino-Torres J, Pérez A, Cebrián-Cuenca A, Párraga-Martínez I, Ávila-Lachica L, Rojo-Martínez G, Pomares-Gómez F, Álvarez-Guisasola F, Sánchez-Molla M, Gutiérrez F, Ortega F, Mata-Cases M, Carretero-Anibarro E, Vilaseca J, Quesada J. Diabetes Does Not Increase the Risk of Hospitalization Due to COVID-19 in Patients Aged 50 Years or Older in Primary Care—APHOSDIAB—COVID-19 Multicenter Study. Journal of Clinical Medicine 2022;11(8):2092 View
  7. Jiang Z, Han Y, Xu L, Shi D, Liu R, Ouyang J, Cai F. The NEAT Equating Via Chaining Random Forests in the Context of Small Sample Sizes: A Machine-Learning Method. Educational and Psychological Measurement 2023;83(5):984 View
  8. Yang H, Nguyen T, Chuang T. An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases. Tropical Medicine and Infectious Disease 2023;8(4):238 View
  9. Ge J, Digitale J, Fenton C, McCulloch C, Lai J, Pletcher M, Gennatas E. Predicting post–liver transplant outcomes in patients with acute-on-chronic liver failure using Expert-Augmented Machine Learning. American Journal of Transplantation 2023;23(12):1908 View
  10. Zhu Y, Yu B, Tang K, Liu T, Niu D, Zhang L. Development and validation of a prediction model based on comorbidities to estimate the risk of in-hospital death in patients with COVID-19. Frontiers in Public Health 2023;11 View
  11. Lohaj O, Paralič J, Bednár P, Paraličová Z, Huba M. Unraveling COVID-19 Dynamics via Machine Learning and XAI: Investigating Variant Influence and Prognostic Classification. Machine Learning and Knowledge Extraction 2023;5(4):1266 View
  12. Chandra S, Bajpai M. Efficient Machine Learning and Factional Calculus Based Mathematical Model for Early COVID Prediction. Human-Centric Intelligent Systems 2023;3(4):508 View
  13. Ghaderzadeh M, Asadi F, Ramezan Ghorbani N, Almasi S, Taami T. Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics. Iranian Journal of Blood and Cancer 2023;15(3):93 View
  14. Ruiz‐Ochoa D, Guerra‐Ruiz A, García‐Unzueta M, Muñoz‐Cacho P, Rodriguez‐Montalvan B, Amado‐Diago C, Lavín‐Gómez B, Cano‐García M, Pablo‐Marcos D, Vázquez L. Sex hormones and the total testosterone:estradiol ratio as predictors of severe acute respiratory syndrome coronavirus 2 infection in hospitalized men. Andrology 2024 View
  15. Burton R, Raffray L, Moet L, Cuff S, White D, Baker S, Moser B, O’Donnell V, Ghazal P, Morgan M, Artemiou A, Eberl M. Conventional and unconventional T-cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients. Clinical and Experimental Immunology 2024;216(3):293 View

Books/Policy Documents

  1. Iglesias A, Gálvez A, Suárez P. Mathematical Modeling and Intelligent Control for Combating Pandemics. View