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

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.

Automated summary

This study compared the performance of 18 machine learning algorithms in predicting ICU admission and mortality among COVID-19 patients, finding that ensemble-based models outperformed other model types in predicting both 5-day ICU admission and 28-day mortality from COVID-19. Implementing these models could aid in clinical decision-making for future infectious disease outbreaks.