An ensemble prediction model for COVID-19 mortality risk.

ABSTRACT:

Background

It's critical to identify COVID-19 patients with a higher death risk at early stage to give them better hospitalization or intensive care. However, thus far, none of the machine learning models has been shown to be successful in an independent cohort. We aim to develop a machine learning model which could accurately predict death risk of COVID-19 patients at an early stage in other independent cohorts.

Methods

We used a cohort containing 4711 patients whose clinical features associated with patient physiological conditions or lab test data associated with inflammation, hepatorenal function, cardiovascular function, and so on to identify key features. To do so, we first developed a novel data preprocessing approach to clean up clinical features and then developed a

SUBMITTER: Li J 

PROVIDER: S-EPMC9685565 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature