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

Abstract As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVI...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/05e8f4be9b9244a0bd5e8c0dcae77ba7
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spelling	oai:doaj.org-article:05e8f4be9b9244a0bd5e8c0dcae77ba72021-12-02T15:52:55ZComparing machine learning algorithms for predicting ICU admission and mortality in COVID-1910.1038/s41746-021-00456-x2398-6352https://doaj.org/article/05e8f4be9b9244a0bd5e8c0dcae77ba72021-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00456-xhttps://doaj.org/toc/2398-6352Abstract As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (n = 3597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (n = 1711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O2 saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m2, and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.Sonu SubudhiAshish VermaAnkit B. PatelC. Corey HardinMelin J. KhandekarHang LeeDustin McEvoyTriantafyllos StylianopoulosLance L. MunnSayon DuttaRakesh K. JainNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 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 Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19
description	Abstract As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (n = 3597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (n = 1711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O2 saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m2, and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.
format	article
author	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
author_facet	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
author_sort	Sonu Subudhi
title	Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19
title_short	Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19
title_full	Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19
title_fullStr	Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19
title_full_unstemmed	Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19
title_sort	comparing machine learning algorithms for predicting icu admission and mortality in covid-19
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/05e8f4be9b9244a0bd5e8c0dcae77ba7
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Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19

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