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

JMIR Theme Issue: COVID-19 Special Issue (2169) Infectious Diseases (non-STD/STI) (1255) Clinical Information and Decision Making (950) Machine Learning (1056) Decision Support for Health Professionals (772) Theme Issue: Medical Informatics and COVID-19 (98) Innovative Methods in Public Health and Surveillance (250) Outbreak and Pandemic Preparedness and Management (1493) Theme Issue: Apps for COVID-19 (#Apps4Covid) (122) Statistical Methods for Surveillance and Population Health (116)

Published on in Vol 23 , No 3 (2021) :March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25696, first published .
Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study

Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study

Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study

Authors of this article:

Yingxiang Huang 1 Author Orcid Image ;   Dina Radenkovic 2, 3 Author Orcid Image ;   Kevin Perez 1 Author Orcid Image ;   Kari Nadeau 4 Author Orcid Image ;   Eric Verdin 1 Author Orcid Image ;   David Furman 1 Author Orcid Image
Article Authors Cited by (3) Tweetations (12) Metrics

Yingxiang Huang   1 , PhD ;   Dina Radenkovic   2, 3 , MD ;   Kevin Perez   1 , MSc ;   Kari Nadeau   4 , MD, PhD ;   Eric Verdin   1 , PhD ;   David Furman   1 , PhD

1 Buck Institute for Research on Aging, Novato, CA, US

2 Guy’s & St Thomas’ NHS Foundation Trust, London, GB

3 King’s College London, London, GB

4 Division of Pulmonary, Allergy and Critical Care Medicine, Sean N Parker Center for Allergy and Asthma Research, Stanford University, Stanford, CA, US

Corresponding Author: