Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes.

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clini...

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Main Authors: Gorka Lasso, Saad Khan, Stephanie A Allen, Margarette Mariano, Catalina Florez, Erika P Orner, Jose A Quiroz, Gregory Quevedo, Aldo Massimi, Aditi Hegde, Ariel S Wirchnianski, Robert H Bortz, Ryan J Malonis, George I Georgiev, Karen Tong, Natalia G Herrera, Nicholas C Morano, Scott J Garforth, Avinash Malaviya, Ahmed Khokhar, Ethan Laudermilch, M Eugenia Dieterle, J Maximilian Fels, Denise Haslwanter, Rohit K Jangra, Jason Barnhill, Steven C Almo, Kartik Chandran, Jonathan R Lai, Libusha Kelly, Johanna P Daily, Olivia Vergnolle
Formato: Artigo
Idioma:Inglês
Publicado em: Public Library of Science (PLoS) 2022-01-01
Colecção:PLoS Computational Biology
Acesso em linha:https://doi.org/10.1371/journal.pcbi.1009778
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