Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis.

ABSTRACT:

Background

Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis.

Methods

Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluat

SUBMITTER: Zeng Z 

PROVIDER: S-EPMC8365981 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature