Analysis of COVID-19 inpatients in France during first lockdown of 2020 using explainability methods

The COVID-19 pandemic rapidly puts a heavy pressure on hospital centers, especially on intensive care units. There was an urgent need for tools to understand typology of COVID-19 patients and identify those most at risk of aggravation during their hospital stay. Data included more than 400 patients...

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Veröffentlicht in:	Medical & biological engineering & computing 2022-06, Vol.60 (6), p.1647-1658
Hauptverfasser:	Excoffier, Jean-Baptiste, Salaün-Penquer, Noémie, Ortala, Matthieu, Raphaël-Rousseau, Mathilde, Chouaid, Christos, Jung, Camille
Format:	Artikel
Sprache:	eng
Schlagworte:	Age Artificial intelligence Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Clustering Communicable Disease Control Computer Applications Coronaviruses COVID-19 Eosinophils Hospitals Human Physiology Humans Imaging Inpatients Intensive care units Leukocytes (eosinophilic) Machine learning Original Original Article Pandemics Patients Radiology Retrospective Studies Risk analysis Risk factors SARS-CoV-2 Subgroups Typology
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