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

Nusrat J Epsi(Henry M. Jackson Foundation), Timothy Burgess(Uniformed Services University of the Health Sciences), Evan Ewers(Alexander T. Augusta Military Medical Center), Katrin Mende, Tahaniyat Lalani(Henry M. Jackson Foundation), Jeffrey Livezey(Uniformed Services University of the Health Sciences), Catherine M Berjohn(Henry M. Jackson Foundation), Ryan C. Maves(Naval Medical Center San Diego), Nikhil Huprikar(Uniformed Services University of the Health Sciences), Rupal Mody(William Beaumont Army Medical Center), Derek Larson(Alexander T. Augusta Military Medical Center), John H. Powers(Leidos (United States)), Mark P. Simons(Uniformed Services University of the Health Sciences), Milissa U. Jones(Uniformed Services University of the Health Sciences), Rhonda E Colombo(Henry M. Jackson Foundation), David A Lindholm(Uniformed Services University of the Health Sciences), David Saunders(Uniformed Services University of the Health Sciences), Josh Chenoweth(Henry M. Jackson Foundation), Carlos J Maldonado(Alexander T. Augusta Military Medical Center), Julia S Rozman(Henry M. Jackson Foundation), Allison M. W. Malloy(Uniformed Services University of the Health Sciences), Margaret Edwards(Henry M. Jackson Foundation), David R. Tribble(Uniformed Services University of the Health Sciences), Paul W. Blair(Henry M. Jackson Foundation), Christopher Colombo(Madigan Army Medical Center), Simon Pollett(Henry M. Jackson Foundation), Alfred G. Smith(Naval Medical Center Portsmouth), Samantha Bazan(Carl R. Darnall Army Medical Center), Brian K. Agan(Henry M. Jackson Foundation), Anuradha Ganesan(Walter Reed National Military Medical Center)
PLoS ONE
February 9, 2023
Cited by 21


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