Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

ABSTRACT:

Background

Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.

Methods

A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape

SUBMITTER: Magunia H 

PROVIDER: S-EPMC8370055 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature