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Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19

Medicine and Health

Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19

S. Subudhi, A. Verma, et al.

This intriguing study dives into predicting COVID-19 outcomes using 18 machine learning algorithms. Discover how ensemble models outshine others, revealing key biomarkers for ICU admission and mortality, all thanks to the research conducted by Sonu Subudhi, Ashish Verma, Ankit B. Patel, C. Corey Hardin, Melin J. Khandekar, Hang Lee, Dustin McEvoy, Triantafyllos Stylianopoulos, Lance L. Munn, Sayon Dutta, and Rakesh K. Jain.

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Playback language: English
Abstract
Predicting COVID-19 trajectory is challenging. This study compares 18 machine learning algorithms for predicting ICU admission and mortality. Ensemble models performed best, with CRP, LDH, and O₂ saturation important for ICU admission, and eGFR <60 ml/min/1.73 m², neutrophil and lymphocyte percentages important for mortality prediction. These models could aid clinical decision-making.
Publisher
npj Digital Medicine
Published On
May 21, 2021
Authors
Sonu Subudhi, Ashish Verma, Ankit B. Patel, C. Corey Hardin, Melin J. Khandekar, Hang Lee, Dustin McEvoy, Triantafyllos Stylianopoulos, Lance L. Munn, Sayon Dutta, Rakesh K. Jain
Tags
COVID-19
machine learning
ICU admission
mortality prediction
biomarkers
ensemble models

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