A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure.

ABSTRACT:

Background

Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units.

Methods

Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients' clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation.

Results

The performance of the m

SUBMITTER: Luo C 

PROVIDER: S-EPMC8932070 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature