This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2388) Artificial Intelligence (1190) Machine Learning (1374) Decision Support for Health Professionals (1130) New Methods (903) Infectious Diseases (non-STD/STI) (1481) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Clinical Information and Decision Making (1317) Outbreak and Pandemic Preparedness and Management (1673) Theme Issue: Medical Informatics and COVID-19 (103)

Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23026, first published .
Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

Authors of this article:

Shengtian Sang1 Author Orcid Image ;   Ran Sun1 Author Orcid Image ;   Jean Coquet1 Author Orcid Image ;   Harris Carmichael2 Author Orcid Image ;   Tina Seto3 Author Orcid Image ;   Tina Hernandez-Boussard1 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

Shengtian Sang   1 * , PhD ;   Ran Sun   1 * , PhD, RN ;   Jean Coquet   1 * , PhD ;   Harris Carmichael   2 , MD ;   Tina Seto   3 , MSc ;   Tina Hernandez-Boussard   1 , PhD, MPH, MSc

1 Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States

2 Intermountain Health, Salt Lake City, UT, United States

3 Technology and Digital Solutions, Stanford University, Stanford, CA, United States

*these authors contributed equally

Corresponding Author:

  • Tina Hernandez-Boussard, PhD, MPH, MSc
  • Department of Medicine
  • Biomedical Informatics
  • Stanford University
  • 1265 Welch Rd, 245
  • Stanford, CA, 94305-5479
  • United States
  • Phone: 1 650-725-5507
  • Email: boussard@stanford.edu