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

Formative Evaluation of Digital Health Interventions (3229) Machine Learning (2076) Theme Issue: Medical Informatics and COVID-19 (124) Early Results from COVID-19 Studies (363) Respiratory Medicine (157)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66509, first published .
Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study

Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study

Oxidative Stress Markers and Prediction of Severity With a Machine Learning Approach in Hospitalized Patients With COVID-19 and Severe Lung Disease: Observational, Retrospective, Single-Center Feasibility Study

Authors of this article:

Olivier Raspado1 Author Orcid Image ;   Michel Brack2 Author Orcid Image ;   Olivier Brack3 Author Orcid Image ;   Mélanie Vivancos4 Author Orcid Image ;   Aurélie Esparcieux1 Author Orcid Image ;   Emmanuelle Cart-Tanneur5 Author Orcid Image ;   Abdellah Aouifi1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

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  3. Lin J, Lin P, Liao P, Chen C, Huang H. The association between serum copper-to‑zinc ratio and mortality among older patients in the emergency department: A prospective cohort study in Taiwan. Experimental Gerontology 2025;212:112963 View