Identification of patient demographic, clinical, and SARS-CoV-2 genomic factors associated with severe COVID-19 using supervised machine learning: a retrospective multicenter study

Identification of patient demographic, clinical, and SARS-CoV-2 genomic factors associated with severe COVID-19 using supervised machine learning: a retrospective multicenter study

Abstract Background Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus complicating the prediction of clinical ou...

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Main Authors: Kuganya Nirmalarajah, Patryk Aftanas, Shiva Barati, Emily Chien, Gloria Crowl, Amna Faheem, Lubna Farooqi, Alainna J. Jamal, Saman Khan, Jonathon D. Kotwa, Angel X. Li, Mohammad Mozafarihashjin, Jalees A. Nasir, Altynay Shigayeva, Winfield Yim, Lily Yip, Xi Zoe Zhong, Kevin Katz, Robert Kozak, Andrew G. McArthur, Nick Daneman, Finlay Maguire, Allison J. McGeer, Venkata R. Duvvuri, Samira Mubareka
Format: Article
Language:English
Published: BMC 2025-01-01
Series:BMC Infectious Diseases
Subjects:
COVID-19
SARS-CoV-2
Machine learning
Viral genomics
Disease severity
Data integration
Online Access:https://doi.org/10.1186/s12879-025-10450-3
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https://doi.org/10.1186/s12879-025-10450-3

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