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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Detalles Bibliográficos
Autores principales: Lasso, Gorka, Khan, Saad, Allen, Stephanie A., Mariano, Margarette, Florez, Catalina, Orner, Erika P., Quiroz, Jose A., Quevedo, Gregory, Massimi, Aldo, Hegde, Aditi, Wirchnianski, Ariel S., Bortz, Robert H., Malonis, Ryan J., Georgiev, George I., Tong, Karen, Herrera, Natalia G., Morano, Nicholas C., Garforth, Scott J., Malaviya, Avinash, Khokhar, Ahmed, Laudermilch, Ethan, Dieterle, M. Eugenia, Fels, J. Maximilian, Haslwanter, Denise, Jangra, Rohit K., Barnhill, Jason, Almo, Steven C., Chandran, Kartik, Lai, Jonathan R., Kelly, Libusha, Daily, Johanna P., Vergnolle, Olivia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812869/
https://www.ncbi.nlm.nih.gov/pubmed/35041647
http://dx.doi.org/10.1371/journal.pcbi.1009778

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812869/
https://www.ncbi.nlm.nih.gov/pubmed/35041647
http://dx.doi.org/10.1371/journal.pcbi.1009778

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