title: Forecasting hospital census at a tertiary care children's hospital.
abstract: Developing a forecasting tool for patient census allows for improved staffing, better resource utilization and mobilization, and improved timing of educational campaigns around the disease control process. Using a neural network approach we evaluated several different models and variables for predicting patient census prospectively. These initial studies enabled selection of a subset of predictor variables and show that different network models, and variables must be used based on the season.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
authors.forename: Nephi
authors.initials: N
authors.lastname: Walton
authors.forename: Mollie R
authors.initials: MR
authors.lastname: Poynton
authors.forename: Chris
authors.initials: C
authors.lastname: Maloney
authors.forename: Per H
authors.initials: PH
authors.lastname: Gesteland
mesh_terms: D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005544:Forecasting; D006760:Hospitalization / Q000706:statistics & numerical data*; D006776:Hospitals, Pediatric / Q000706:statistics & numerical data; D006801:Humans; D016571:Neural Networks, Computer; D012141:Respiratory Tract Infections