Application of a machine learning model for early prediction of in-hospital cardiac arrests: Retrospective observational cohort study

Objective: To describe the results of the application of a Machine Learning (ML) model to predict in-hospital cardiac arrests (ICA) 24 hours in advance in the hospital wards. Design: Retrospective observational cohort study. Setting: Hospital Wards. Patients: Data were extracted from the hospital's Electronic Health Record (EHR). The resulting database contained a total of 750 records corresponding to 620 different patients (370 patients with ICA and 250 control), between may 2009 and december 2021. Interventions: No. Main variables of interest: As predictors of ICA, a set of 28 variables including personal history, vital signs and laboratory data was employed. Models: For the early prediction of ICA, predictive models based on the following ML algorithms and using the mentioned variables, were developed and compared: K Nearest Neighbours, Support Vector Machine, Multilayer Perceptron, Random Forest, Gradient Boosting and Custom Ensemble of Gradient Boosting estimators (CEGB).