Use of machine learning algorithms to predict life-threatening ventricular arrhythmia in sepsis.

ABSTRACT:

Aims

Life-threatening ventricular arrhythmias (LTVAs) are common manifestations of sepsis. The majority of sepsis patients with LTVA are unresponsive to initial standard treatment and thus have a poor prognosis. There are very limited studies focusing on the early identification of patients at high risk of LTVA in sepsis to perform optimal preventive treatment interventions. We aimed to develop a prediction model to predict LTVA in sepsis using machine learning (ML) approaches.

Methods and results

Six ML algorithms including CatBoost, LightGBM, and XGBoost were employed to perform the model fitting. The least absolute shrinkage and selection operator (LASSO) regression was used to identify key features. Methods of model evaluation involved in this study included area under t

SUBMITTER: Li L 

PROVIDER: S-EPMC10232270 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature