Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents

Authors

Alon Geva, Harvard University
Philip C Spinella, Washington University School of Medicine in St. Louis
et al

Journal

EClinicalMedicine

Publication Date

10-1-2021

Volume

40

First Page

101112

Document Type

Open Access Publication

DOI

10.1016/j.eclinm.2021.101112

Rights and Permissions

Geva A, Patel MM, Newhams MM, Young CC, Son MBF, Kong M, Maddux AB, Hall MW, Riggs BJ, Singh AR, Giuliano JS, Hobbs CV, Loftis LL, McLaughlin GE, Schwartz SP, Schuster JE, Babbitt CJ, Halasa NB, Gertz SJ, Doymaz S, Hume JR, Bradford TT, Irby K, Carroll CL, McGuire JK, Tarquinio KM, Rowan CM, Mack EH, Cvijanovich NZ, Fitzgerald JC, Spinella PC, Staat MA, Clouser KN, Soma VL, Dapul H, Maamari M, Bowens C, Havlin KM, Mourani PM, Heidemann SM, Horwitz SM, Feldstein LR, Tenforde MW, Newburger JW, Mandl KD, Randolph AG; Overcoming COVID-19 Investigators. Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents. EClinicalMedicine. 2021 Oct;40:101112. doi: 10.1016/j.eclinm.2021.101112. © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Recommended Citation

Geva, Alon; Spinella, Philip C; and et al, "Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents." EClinicalMedicine. 40, 101112 (2021).
https://digitalcommons.wustl.edu/open_access_pubs/10850