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

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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Bibliographic Details
Main Authors: Chouaid, C. (Author), Excoffier, J.-B (Author), Jung, C. (Author), Ortala, M. (Author), Raphaël-Rousseau, M. (Author), Salaün-Penquer, N. (Author)
Format: Article
Language:English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
Biological features
Biological variables
Chest CT
Clinical features
COVID-19
Explainable artificial intelligence
Important features
Instance selection
Intensive care units
Machine learning
Nonlinear effect
Risk score
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