Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan.

ABSTRACT:

Background

Machine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patients.

Methods

We retrospectively included patients who were admitted to intensive care units during 2015-2018 at a tertiary hospital in central Taiwan and linked with the Taiwanese nationwide death registration data. Three ML models, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR), were used to establish mortality prediction model. Furthermore, we used feature importance, Shapley Additive exPlanations (SHAP) plot, partial dependence plot

SUBMITTER: Chan MC 

PROVIDER: S-EPMC8953968 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature