Article
A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms.
PloS one - 1 Jan 2023
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
Abstract excerpt
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 inflammatory profiles. METHODS: 1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative...
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