Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

Journal: EBioMedicine
PMID:

Abstract

BACKGROUND: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID.

Authors

  • Timothy Bergquist
    Sage Bionetworks, Seattle, WA, USA.
  • Johanna Loomba
    University of Virginia, Charlottesville, VA, USA.
  • Emily Pfaff
    University of North Carolina, Chapel Hill, NC, USA.
  • Fangfang Xia
    Computing, Environment, and Life Sciences Directorate, Argonne National Laboratory, Argonne, Illinois, USA.
  • Zixuan Zhao
    School of Public Health, Shandong Second Medical University, Weifang, Shandong, China.
  • Yitan Zhu
    Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL, 60439, USA. yitan.zhu@anl.gov.
  • Elliot Mitchell
    Geisinger Health System, New York, NY, USA.
  • Biplab Bhattacharya
    Geisinger Health System, New York, NY, USA.
  • Gaurav Shetty
    Geisinger Health System, New York, NY, USA.
  • Tamanna Munia
    Geisinger Health System, New York, NY, USA.
  • Grant Delong
    Geisinger Health System, New York, NY, USA.
  • Adbul Tariq
    Geisinger Health System, New York, NY, USA.
  • Zachary Butzin-Dozier
    University of California Berkeley, Berkeley, CA, USA.
  • Yunwen Ji
    University of California Berkeley, Berkeley, CA, USA.
  • Haodong Li
    Divisions of Epidemiology & Biostatistics, University of California, Berkeley, Berkeley, California, USA.
  • Jeremy Coyle
  • Seraphina Shi
    University of California Berkeley, Berkeley, CA, USA.
  • Rachael V Philips
    University of California Berkeley, Berkeley, CA, USA.
  • Andrew Mertens
    Department of Epidemiology, University of California, Berkeley, Berkeley, USA.
  • Romain Pirracchio
  • Mark van der Laan
  • John M Colford
    Department of Epidemiology, University of California, Berkeley, Berkeley, USA.
  • Alan Hubbard
    Division of Biostatistics, University of California, Berkeley, California, United States of America.
  • Jifan Gao
    Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison.
  • Guanhua Chen
    Vanderbilt University School of Medicine, Nashville, TN.
  • Neelay Velingker
    University of Pennsylvania, Philadelphia, PA, USA.
  • Ziyang Li
    University of Pennsylvania, Philadelphia, PA, USA.
  • Yinjun Wu
    University of Pennsylvania, Philadelphia, PA, USA.
  • Adam Stein
    University of Pennsylvania, Philadelphia, PA, USA.
  • Jiani Huang
    University of Pennsylvania, Philadelphia, PA, USA.
  • Zongyu Dai
    University of Pennsylvania, Philadelphia, PA, USA.
  • Qi Long
    Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.
  • Mayur Naik
    University of Pennsylvania, Philadelphia, PA, USA.
  • John Holmes
    University of Pennsylvania, Philadelphia, PA, USA.
  • Danielle Mowery
    Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, 84108 UT United States.
  • Eric Wong
    University of Pennsylvania, Philadelphia, PA, USA.
  • Ravi Parekh
    University of Pennsylvania, Philadelphia, PA, USA.
  • Emily Getzen
    University of Pennsylvania, Philadelphia, PA, USA.
  • Jake Hightower
    Ruvos, Tallahassee, FL, USA.
  • Jennifer Blase
    Ruvos, Tallahassee, FL, USA.