Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan.

ABSTRACT:

Background

Weaning from mechanical ventilation (MV) is an essential issue in critically ill patients, and we used an explainable machine learning (ML) approach to establish an extubation prediction model.

Methods

We enrolled patients who were admitted to intensive care units during 2015-2019 at Taichung Veterans General Hospital, a referral hospital in central Taiwan. We used five ML models, including extreme gradient boosting (XGBoost), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), random forest (RF) and logistic regression (LR), to establish the extubation prediction model, and the feature window as well as prediction window was 48 h and 24 h, respectively. We further employed feature importance, Shapley additive explanations (SHAP) plot, par

SUBMITTER: Pai KC 

PROVIDER: S-EPMC9664699 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature