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, Milissa U. Jones, Samantha E. Bazan, Rhonda E. Colombo, Christopher J. Colombo, Evan C. Ewers, Derek T. Larson, Catherine M. Berjohn, Carlos J. Maldonado, Paul W. Blair, Josh ChenowethDavid L. Saunders, Jeffrey Livezey, Ryan C. Maves, Margaret Sanchez Edwards, Julia S. Rozman, Mark P. Simons, David R. Tribble, Brian K. Agan, Timothy H. Burgess, Simon D. Pollett, Jessica J. Cowden, Teresa M. Merritt, Nora Elnahas, Christa Glinn, Donna Jennings, Celia Byrne, Jennifer Rusiecki, Ann Scher, D. Lindholm, C. Colombo, R. Colombo, C. Mount, C. Schofield, M. Stein, T. Lalani, C. Berjohn, K. Chung, C. Olsen, S. Richard*, M. Simons

*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

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