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

JMIR Theme Issue: COVID-19 Special Issue (2457) Machine Learning (1485) Outbreak and Pandemic Preparedness and Management (1737) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1519) Clinical Information and Decision Making (1423) Infectious Diseases (non-STD/STI) (1584)

Published on in Vol 23, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25988, first published .
Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

Authors of this article:

Julián Benito-León1 Author Orcid Image ;   Mª Dolores del Castillo2 Author Orcid Image ;   Alberto Estirado3 Author Orcid Image ;   Ritwik Ghosh4 Author Orcid Image ;   Souvik Dubey5 Author Orcid Image ;   J Ignacio Serrano2 Author Orcid Image
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