Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study.

Journal: Journal of medical Internet research

PMID: 33872186

BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19.

Julián Benito-León

Department of Neurology, University Hospital "12 de Octubre", Madrid, Spain.
Mª Dolores Del Castillo

Neural and Cognitive Engineering Group, Center for Automation and Robotics, CSIC-UPM, Arganda del Rey, Spain.
Alberto Estirado

HM Hospitales, Madrid, Spain.
Ritwik Ghosh

Department of General Medicine, Burdwan Medical College and Hospital, Burdwan, India.
Souvik Dubey

Department of Neuromedicine, Bangur Institute of Neurosciences, Kolkata, India.
J Ignacio Serrano

Neural and Cognitive Engineering Group, Center for Automation and Robotics, CSIC-UPM, Arganda del Rey, Spain.

Alanine Transaminase Aspartate Aminotransferases C-Reactive Protein Cell Count Cluster Analysis COVID-19 Datasets as Topic Emergency Service, Hospital Humans L-Lactate Dehydrogenase Longitudinal Studies Lymphocytes Monocytes Neutrophils Prognosis Spain Triage Unsupervised Machine Learning

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