학술논문
Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning
Document Type
article
Author
Yvonne M. Mueller; Thijs J. Schrama; Rik Ruijten; Marco W. J. Schreurs; Dwin G. B. Grashof; Harmen J. G. van de Werken; Giovanna Jona Lasinio; Daniel Álvarez-Sierra; Caoimhe H. Kiernan; Melisa D. Castro Eiro; Marjan van Meurs; Inge Brouwers-Haspels; Manzhi Zhao; Ling Li; Harm de Wit; Christos A. Ouzounis; Merel E. P. Wilmsen; Tessa M. Alofs; Danique A. Laport; Tamara van Wees; Geoffrey Kraker; Maria C. Jaimes; Sebastiaan Van Bockstael; Manuel Hernández-González; Casper Rokx; Bart J. A. Rijnders; Ricardo Pujol-Borrell; Peter D. Katsikis
Source
Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)
Subject
Language
English
ISSN
2041-1723
Abstract
Developing predictive methods to identify patients with high risk of severe COVID-19 disease is of crucial importance. Authors show here that by measuring anti-SARS-CoV-2 antibody and cytokine levels at the time of hospital admission and integrating the data by unsupervised hierarchical clustering/machine learning, it is possible to predict unfavourable outcome.