title: Predicting hospital admission for Emergency Department patients using a Bayesian network.
abstract: Hospital admission delays in the Emergency Department (ED) reduce volume capacity and contribute to the nation's ED diversion problem. This study evaluated the accuracy of a Bayesian network for the early prediction of hospital admission status using data from 16,900 ED encounters. The final model included nine variables that are commonly available in many ED settings. The area under the receiver operating characteristic curve was 0.894 (95% CI: 0.887-0.902) for the validation set. The system had high accuracy an may be used to alert clinicians to initiate admission processes earlier during a patient's ED encounter.
authors.affiliation: Dept. of Computer Science, University of Birmingham, Birmingham, 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: D019540:Area Under Curve; D001499:Bayes Theorem; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D006801:Humans; D016571:Neural Networks, Computer; D010343:Patient Admission