title: Predicting hospital admission in a pediatric Emergency Department using an Artificial Neural Network.
abstract: Hospital admission delays in the Emergency Department (ED) reduce capacity and contribute to the ED's diversion problem. We evaluated the accuracy of an Artificial Neural Network for the early prediction of hospital admission using data from 43,077 pediatric ED encounters. We used 9 variables commonly available in the ED setting. The area under the receiver operating characteristic curve was 0.897 (95% CI: 0.887-0.896). The instrument demonstrated high accuracy and may be used to alert clinicians to initiate admission processes earlier during a patient's ED encounter.
authors.affiliation: Dept. of Informatics, University of Edinburgh, Edinburgh, UK.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Leegon
authors.forename: Ian
authors.initials: I
authors.lastname: Jones
authors.forename: Kevin
authors.initials: K
authors.lastname: Lanaghan
authors.forename: Dominik
authors.initials: D
authors.lastname: Aronsky
mesh_terms: D002648:Child; D004636:Emergency Service, Hospital / Q000458:organization & administration; D006801:Humans; D016571:Neural Networks, Computer; D010343:Patient Admission; D012372:ROC Curve