Machine learning-based risk prediction of outcomes in patients hospitalized with COVID-19 in Australia: the AUS-COVID Score.

ABSTRACT:

Objectives

We aimed to develop a highly interpretable and effective, machine learning (ML)-based risk prediction algorithm to predict in-hospital mortality, intubation, and adverse cardiovascular events in patients hospitalized with coronavirus disease 2019 (COVID-19) in Australia (AUS-COVID Score).

Materials and methods

This prospective study across 21 hospitals included 1714 consecutive patients aged ≥ 18 in their index hospitalization with COVID-19. The dataset was separated into training (80%) and test sets (20%). Eight supervised ML methods were used: least absolute shrinkage and selection operator (LASSO), ridge, elastic net (EN), decision tree, support vector machine, random forest, AdaBoost, and gradient boosting. A feature selection method was used to establish info

SUBMITTER: Sritharan HP 

PROVIDER: S-EPMC12758480 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature