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

Formative Evaluation of Digital Health Interventions (2768) Machine Learning (1683) Theme Issue: Medical Informatics and COVID-19 (117) Early Results from COVID-19 Studies (332) Respiratory Medicine (113)

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 Tweetations Metrics

Olivier Raspado   1 , Dr Med ;   Michel Brack   2 , Dr Med ;   Olivier Brack   3 ;   Mélanie Vivancos   4 ;   Aurélie Esparcieux   1 , Dr Med ;   Emmanuelle Cart-Tanneur   5 , Dr Bio ;   Abdellah Aouifi   1 , Dr Med

1 Infirmerie Protestante, Caluire-et-Cuire, France

2 Oxidative Stress College, La Garenne-Colombes, France

3 Statistique Industrielle Khi² Consulting (KSIC), Bayet, France

4 Clinical Research and Innovation Department, Infirmerie Protestante, Caluire-et-Cuire, France

5 Eurofins Biomnis Laboratory, Lyon, France

Corresponding Author: