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Original research
Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs

Authors

  • Christos A Makridis National Artificial Intelligence Institute at the Department of Veterans Affairs, US Department of Veterans Affairs, Washington, District of Columbia, USADigital Economy Lab, Stanford University, Stanford University, Stanford, California, USA PubMed articlesGoogle scholar articles
  • Tim Strebel Washington D.C. VA Medical Center, Department of Veterans Affairs, Washington, District of Columbia, USA PubMed articlesGoogle scholar articles
  • Vincent Marconi Rollins School of Public Health, Emory University, Atlanta, Georgia, USA PubMed articlesGoogle scholar articles
  • Gil Alterovitz National Artificial Intelligence Institute at the Department of Veterans Affairs, US Department of Veterans Affairs, Washington, District of Columbia, USAHarvard Medical School, Boston, Massachusetts, USA PubMed articlesGoogle scholar articles
  1. Correspondence to Dr Christos A Makridis; christos.makridis{at}va.gov
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Citation

Makridis CA, Strebel T, Marconi V, et al
Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs
BMJ Health & Care Informatics 2021;28:e100312. doi: 10.1136/bmjhci-2020-100312

Publication history

  • Received December 23, 2020
  • Revision received March 17, 2021
  • Accepted March 31, 2021
  • First published June 9, 2021.
Online issue publication 
June 09, 2021

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Copyright information

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.