title: Predicting hospital admission at triage in an emergency department.
abstract: Predicting hospital admission for Emergency Department (ED) patients at the time of triage may improve throughput. To predict admission we created and validated a Bayesian Network from 47,993 encounters (training: n=23,996, validation: n=9,599, test: n=14,398). The area under the receiver operator characteristic curve was 0.833 (0.8260.840) for the network and 0.790 (0.7810.799) for the control variable (acuity only). Predicting hospital admission early during an encounter may help anticipate ED workload and potential overcrowding.
authors.affiliation: Department of Biomedical Informatics,Vanderbilt University Medical Center, Nashville, TN, USA.
authors.forename: Judith W
authors.initials: JW
authors.lastname: Dexheimer
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Leegon
authors.forename: Dominik
authors.initials: D
authors.lastname: Aronsky
mesh_terms: D001499:Bayes Theorem; D004636:Emergency Service, Hospital / Q000458:organization & administration; D006801:Humans; D016571:Neural Networks, Computer; D010343:Patient Admission; D012372:ROC Curve; D014218:Triage