Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm.

ABSTRACT:

Background

Children with congenital and acquired heart disease are at a higher risk of cardiac arrest compared to those without heart disease. Although the monitoring of cardiopulmonary resuscitation quality and extracorporeal resuscitation technologies have advanced, survival after cardiac arrest in this population has not improved. Cardiac arrest prevention, using predictive algorithms with machine learning, has the potential to reduce cardiac arrest rates. However, few studies have evaluated the use of these algorithms in predicting cardiac arrest in children with heart disease.

Methods

We collected demographic, laboratory, and vital sign information from the electronic health records (EHR) of all the patients that were admitted to a single-center pediatric cardiac intens

SUBMITTER: Yu P 

PROVIDER: S-EPMC10095110 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature