Demystifying machine learning for mortality prediction

J. M. Smit*, M. E. van Genderen, M. J.T. Reinders, D. A.M.P.J. Gommers, J. H. Krijthe, J. Van Bommel

*Corresponding author for this work

Research output: Contribution to journalComment/Letter to the editorProfessional

2 Citations (Scopus)
15 Downloads (Pure)


With interest, we read the article by Banoei et al. [1] on machine learning (ML) models to predict mortality among COVID-19 patients. They refer to other studies that failed to predict mortality using ‘conventional statistical analysis’, after which they present a linear ML model as a better suited method for such complex medical problems. We feel such a claim creates an image around ML as an alternative technique that offers solutions where statistical modeling fails. However, ML and statistical modeling are tightly interwoven. [...]
Original languageEnglish
Article number447
JournalCritical Care
Issue number1
Publication statusPublished - 23 Dec 2021


Dive into the research topics of 'Demystifying machine learning for mortality prediction'. Together they form a unique fingerprint.

Cite this