Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.

ABSTRACT: Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs). Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature

SUBMITTER: Zhao QY 

PROVIDER: S-EPMC8165178 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature