Author: Subudhi, Sonu; Verma, Ashish; Patel, Ankit B.; Hardin, C. Corey; Khandekar, Melin J.; Lee, Hang; McEvoy, Dustin; Stylianopoulos, Triantafyllos; Munn, Lance L.; Dutta, Sayon; Jain, Rakesh K.
Title: Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19 Cord-id: re4mo2d3 Document date: 2021_5_21
ID: re4mo2d3
Snippet: 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 =
Document: 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 O(2) saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m(2), 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.
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