학술논문
Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients
Document Type
article
Author
Nathalie Lassau; Samy Ammari; Emilie Chouzenoux; Hugo Gortais; Paul Herent; Matthieu Devilder; Samer Soliman; Olivier Meyrignac; Marie-Pauline Talabard; Jean-Philippe Lamarque; Remy Dubois; Nicolas Loiseau; Paul Trichelair; Etienne Bendjebbar; Gabriel Garcia; Corinne Balleyguier; Mansouria Merad; Annabelle Stoclin; Simon Jegou; Franck Griscelli; Nicolas Tetelboum; Yingping Li; Sagar Verma; Matthieu Terris; Tasnim Dardouri; Kavya Gupta; Ana Neacsu; Frank Chemouni; Meriem Sefta; Paul Jehanno; Imad Bousaid; Yannick Boursin; Emmanuel Planchet; Mikael Azoulay; Jocelyn Dachary; Fabien Brulport; Adrian Gonzalez; Olivier Dehaene; Jean-Baptiste Schiratti; Kathryn Schutte; Jean-Christophe Pesquet; Hugues Talbot; Elodie Pronier; Gilles Wainrib; Thomas Clozel; Fabrice Barlesi; Marie-France Bellin; Michael G. B. Blum
Source
Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
Subject
Language
English
ISSN
2041-1723
Abstract
The SARS-COV-2 pandemic has put pressure on intensive care units, so that predicting severe deterioration early is a priority. Here, the authors develop a multimodal severity score including clinical and imaging features that has significantly improved prognostic performance in two validation datasets compared to previous scores.