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

Clinical Information and Decision Making (1426) JMIR Theme Issue: COVID-19 Special Issue (2458) Machine Learning (1491) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520) Outbreak and Pandemic Preparedness and Management (1740) Infectious Diseases (non-STD/STI) (1588) Artificial Intelligence (1409) Decision Support for Health Professionals (1203)

Published on in Vol 23, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23948, first published .
A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation

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

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

Authors of this article:

Yuanfang Chen1, 2 Author Orcid Image ;   Liu Ouyang3 Author Orcid Image ;   Forrest S Bao4 Author Orcid Image ;   Qian Li5 Author Orcid Image ;   Lei Han1, 6 Author Orcid Image ;   Hengdong Zhang1, 6 Author Orcid Image ;   Baoli Zhu1, 6, 7 Author Orcid Image ;   Yaorong Ge8 Author Orcid Image ;   Patrick Robinson9 Author Orcid Image ;   Ming Xu1, 6, 9 Author Orcid Image ;   Jie Liu10 Author Orcid Image ;   Shi Chen9, 11 Author Orcid Image
Article Authors Cited by (31) Tweetations (6) Metrics

Journals

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  10. Mustafa A. Mohammad R, Aljabri M, Aboulnour M, Mirza S, Alshobaiki A, Ramachandran M. Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques. Applied Computational Intelligence and Soft Computing 2022;2022:1 View
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  15. Tang S, Chen T, Kuo K, Huang J, Kuo C, Chu Y. Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study. Journal of the Chinese Medical Association 2023;86(11):1020 View
  16. Alahmadi A, Alansari A, Alsheikh N, Alshammasi S, Alshamery M, Al-abdulmohsin R, Al Rabia L, Al Nass F, Alghamdi M, Almustafa S, Aljamea Z, Kurdi S, Islam M, Hussein D. Beta blockers may be protective in COVID-19; findings of a study to develop an interpretable machine learning model to assess COVID-19 disease severity in light of clinical findings, medication history, and patient comorbidities. Informatics in Medicine Unlocked 2023;42:101341 View
  17. Salvi M, Loh H, Seoni S, Barua P, García S, Molinari F, Acharya U. Multi-modality approaches for medical support systems: A systematic review of the last decade. Information Fusion 2024;103:102134 View
  18. Chadaga K, Prabhu S, Sampathila N, Chadaga R. Severity prediction in COVID-19 patients using clinical markers and explainable artificial intelligence: A stacked ensemble machine learning approach. Intelligent Decision Technologies 2023;17(4):959 View
  19. 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
  20. Liu P, Xing Z, Peng X, Zhang M, Shu C, Wang C, Li R, Tang L, Wei H, Ran X, Qiu S, Gao N, Yeo Y, Liu X, Ji F. Machine learning versus multivariate logistic regression for predicting severe COVID‐19 in hospitalized children with Omicron variant infection. Journal of Medical Virology 2024;96(2) View
  21. Badiola-Zabala G, Lopez-Guede J, Estevez J, Graña M. Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022. Electronics 2024;13(6):1005 View
  22. Li C, Wu K, Yang R, Liao M, Li J, Zhu Q, Zhang J, Zhang X. Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19. Scientific Reports 2024;14(1) View
  23. Qian F, Cao Y, Liu Y, Huang J, Zhu R. A predictive model to explore risk factors for severe COVID-19. Scientific Reports 2024;14(1) View
  24. Chen W, Yang J, Sun Z, Zhang X, Tao G, Ding Y, Gu J, Bu J, Wang H. DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data. Translational Psychiatry 2024;14(1) View
  25. Kim T, Lee H. Evaluating AI Models and Predictors for COVID-19 Infection Dependent on Data from Patients with Cancer or Not: A Systematic Review. Korean Journal of Clinical Pharmacy 2024;34(3):141 View
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Books/Policy Documents

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