Predicting pediatric emergence delirium using data-driven machine learning applied to electronic health record dataset at a quaternary care pediatric hospital.
Pediatric emergence delirium is an undesirable outcome that is understudied. Development of a predictive model is an initial step toward reducing its occurrence. This study aimed to apply machine learning (ML) methods to a large clinical dataset to develop a predictive model for pediatric emergence delirium.
Author(s): Yu, Han, Simpao, Allan F, Ruiz, Victor M, Nelson, Olivia, Muhly, Wallis T, Sutherland, Tori N, Gálvez, Julia A, Pushkar, Mykhailo B, Stricker, Paul A, Tsui, Fuchiang Rich
DOI: 10.1093/jamiaopen/ooad106