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Epsi NJ, Powers JH, Lindholm DA, Mende K, Malloy A, Ganesan A, Huprikar N, Lalani T, Smith A, Mody RM, Jones MU, Bazan SE, Colombo RE, Colombo CJ, Ewers EC, Larson DT, Berjohn CM, Maldonado CJ, Blair PW, Chenoweth J, Saunders DL, Livezey J, Maves RC, Sanchez Edwards M, Rozman JS, Simons MP, Tribble DR, Agan BK, Burgess TH, Pollett SD, for the EPICC COVID-19 Cohort Study Group. 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 2023;18:e0281272. [PMID: 36757946 PMCID: PMC9910657 DOI: 10.1371/journal.pone.0281272] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/19/2023] [Indexed: 02/10/2023] Open |