Demystifying machine learning for mortality prediction

Affiliations

¹ Department of Intensive Care, Erasmus University Medical Center, Rotterdam, Netherlands. j.smit@erasmusmc.nl.
² EEMCS, Pattern Recognition and Bio-informatics Group, Delft University of Technology, Delft, Netherlands. j.smit@erasmusmc.nl.
³ Department of Intensive Care, Erasmus University Medical Center, Rotterdam, Netherlands.
⁴ EEMCS, Pattern Recognition and Bio-informatics Group, Delft University of Technology, Delft, Netherlands.

Comment

J M Smit et al. Crit Care. 2021.

. 2021 Dec 23;25(1):447.

doi: 10.1186/s13054-021-03868-z.

¹ Department of Intensive Care, Erasmus University Medical Center, Rotterdam, Netherlands. j.smit@erasmusmc.nl.
² EEMCS, Pattern Recognition and Bio-informatics Group, Delft University of Technology, Delft, Netherlands. j.smit@erasmusmc.nl.
³ Department of Intensive Care, Erasmus University Medical Center, Rotterdam, Netherlands.
⁴ EEMCS, Pattern Recognition and Bio-informatics Group, Delft University of Technology, Delft, Netherlands.

No abstract available

Conflict of interest statement

The authors declare that they have no competing interests.

Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying.
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