A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms

BACKGROUND: Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflam...

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Detalles Bibliográficos
Autores principales: Epsi, Nusrat J., Powers, John H., Lindholm, David A., Mende, Katrin, Malloy, Allison, Ganesan, Anuradha, Huprikar, Nikhil, Lalani, Tahaniyat, Smith, Alfred, Mody, Rupal M., Jones, Milissa U., Bazan, Samantha E., Colombo, Rhonda E., Colombo, Christopher J., Ewers, Evan C., Larson, Derek T., Berjohn, Catherine M., Maldonado, Carlos J., Blair, Paul W., Chenoweth, Josh, Saunders, David L., Livezey, Jeffrey, Maves, Ryan C., Sanchez Edwards, Margaret, Rozman, Julia S., Simons, Mark P., Tribble, David R., Agan, Brian K., Burgess, Timothy H., Pollett, Simon D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910657/
https://www.ncbi.nlm.nih.gov/pubmed/36757946
http://dx.doi.org/10.1371/journal.pone.0281272

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910657/
https://www.ncbi.nlm.nih.gov/pubmed/36757946
http://dx.doi.org/10.1371/journal.pone.0281272

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