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

JMIR Theme Issue: COVID-19 Special Issue (1653) Infectious Diseases (non-STD/STI) (1105) Clinical Information and Decision Making (712) Machine Learning (802) Decision Support for Health Professionals (594) Theme Issue: Medical Informatics and COVID-19 (91) Innovative Methods in Public Health and Surveillance (201) Outbreak and Pandemic Preparedness and Management (1247) Theme Issue: Apps for COVID-19 (#Apps4Covid) (108) Statistical Methods for Surveillance and Population Health (98)

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 (1) 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: