Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• NPJ digital medicine, 2021-05, Vol.4 (1), p.87-87, Article 87

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 saturation were important for ICU admission models whereas eGFR <60 ml/min/1.73 m , 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.