Survival prediction for heart failure complicated by sepsis: based on machine learning methods.

ABSTRACT:

Background

Heart failure is a cardiovascular disorder, while sepsis is a common non-cardiac cause of mortality. Patients with combined heart failure and sepsis have a significantly higher mortality rate and poor prognosis, making early identification of high-risk patients and appropriate allocation of medical resources critically important.

Methods

We constructed a survival prediction model for patients with heart failure and sepsis using the eICU-CRD database and externally validated it using the MIMIC-IV database. Our primary outcome is the 28-day all-cause mortality rate. The Boruta method is used for initial feature selection, followed by feature ranking using the XGBoost algorithm. Four machine learning models were compared, including Logistic Regression (LR), eXtreme G

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC11484001 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature