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A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms
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Nusrat J. Epsi,
Roles Data curation, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing
Affiliations Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, United States of America
⨯ - John H. Powers,
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David A. Lindholm,
Roles Supervision
Affiliations Molecular Biology Laboratory, Brooke Army Medical Center, San Antonio, Texas, United States of America, Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America
⨯ - Katrin Mende,
- Allison Malloy,
- Anuradha Ganesan,
- Nikhil Huprikar,
- Tahaniyat Lalani,
- Alfred Smith,
- Rupal M. Mody,
- Milissa U. Jones,
- Samantha E. Bazan,
- Rhonda E. Colombo,
- Christopher J. Colombo,
- Evan C. Ewers,
- Derek T. Larson,
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Catherine M. Berjohn,
Roles Resources
Affiliations Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America, Department of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America, Infectious Disease Clinic, Naval Medical Center San Diego, San Diego, California, United States of America
⨯ -
Carlos J. Maldonado,
Roles Resources
Affiliation Department of Research and Clinical Investigation, Womack Army Medical Center, Fort Bragg, North Carolina, United States of America
⨯ - Paul W. Blair,
- Josh Chenoweth,
- David L. Saunders,
- Jeffrey Livezey,
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Ryan C. Maves,
Roles Resources
Affiliation Infectious Diseases and Critical Care Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
⨯ - Margaret Sanchez Edwards,
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Julia S. Rozman,
Roles Project administration
Affiliations Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, United States of America
⨯ - Mark P. Simons,
- David R. Tribble,
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Brian K. Agan,
Roles Funding acquisition, Project administration
Affiliations Infectious Disease Clinical Research Program, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America, Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, United States of America
⨯ - Timothy H. Burgess,
- Simon D. Pollett,
- [ ... ],
- for the EPICC COVID-19 Cohort Study Group
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https://orcid.org/0000-0001-5363-075X
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,
- John H. Powers,
- David A. Lindholm,
- Katrin Mende,
- Allison Malloy,
- Anuradha Ganesan,
- Nikhil Huprikar,
- Tahaniyat Lalani,
- Alfred Smith,
- Rupal M. Mody
- Published: February 9, 2023
- https://doi.org/10.1371/journal.pone.0281272