Retrospective development and external validation of machine learning models for COVID-19 severity prediction in lung and hematological malignancies: evidence of subtype-specific feature selection bias in joint training
Retrospective development and external validation of machine learning models for COVID-19 severity prediction in lung and hematological malignancies: evidence of subtype-specific feature selection bias in joint training
Souad Assaad, Guillaume Chassagnon, Basile Bertrand, Frederik Trinkmann et al.
Journal
BMC Infectious Diseases
Topics
COVID-19 Clinical Research Studies
article
Retrospective development and external validation of machine learning models for COVID-19 severity prediction in lung and hematological malignancies: evidence of subtype-specific feature selection bias in joint training
Souad Assaad, Guillaume Chassagnon, Basile Bertrand, Frederik Trinkmann, Walid Kandeil, Jean‐Yves Blay, Samir Bouam, Olivier Teboul, Catherine Martineau-Huynh, David Dellamonica, Daniel Pescia, Amanda Remorino, Hugo Crochet, Camille Bigenwald, Fungwe Jah, Pierre-Étienne Heudel, Enzo Batistella, Laurence Albiges, Fabrice Andre, Marie Guillet, Enzo D’Amico-Gandia, Marie-Pierre Revel, Nikos Paragios
article en
Abstract
No abstract available for this paper.
Publication Tag
BMC Infectious Diseases
Institutions
Université Claude Bernard Lyon 1 (FR), AstraZeneca (United Kingdom) (GB), Broad Institute (US), Inserm (FR), University of Mannheim (DE), Université Paris Cité (FR), Heidelberg University (DE), Université Paris-Saclay (FR), Institut Gustave Roussy (FR), University Hospital Heidelberg (DE), Assistance Publique – Hôpitaux de Paris (FR), Maternité Port Royal (FR), Centre Léon Bérard (FR), CentraleSupélec (FR), Groupe Hospitalier Cochin - Port-Royal, Hôtel-Dieu, Broca - La Collégiale (FR), German Center for Lung Research (DE), University Medical Centre Mannheim (DE), Protéomique, Réponse Inflammatoire et Spectrométrie de Masse (FR)