title: A retrospective analysis of the utility of an artificial neural network to predict ED volume.
abstract: OBJECTIVE
  The objectives of this study are to design an artificial neural network (ANN) and to test it retrospectively to determine if it may be used to predict emergency department (ED) volume.
  
  
  METHODS
  We conducted a retrospective review of patient registry data from February 4, 2007, to December 31, 2009, from an inner city, tertiary care hospital. We harvested data regarding weather, days of week, air quality, and special events to train the ANN. The ANN belongs to a class of neural networks called multilayer perceptrons. We designed an ANN composed of 37 input neurons, 22 hidden neurons, and 1 output neuron designed to predict the daily number of ED visits. The training method is a supervised backpropagation algorithm that uses mean squared error to minimize the average squared error between the ANN's output and the number of ED visits over all the example pairs.
  
  
  RESULTS
  A linear regression between the predicted and actual ED visits demonstrated an R2 of 0.957 with a slope of 0.997. Ninety-five percent of the time, the ANN was within 20 visits.
  
  
  CONCLUSIONS
  The results of this study show that a properly designed ANN is an effective tool that may be used to predict ED volume. The scatterplot demonstrates that the ANN is least predictive at the extreme ends of the spectrum suggesting that the ANN may be missing important variables. A properly calibrated ANN may have the potential to allow ED administrators to staff their units more appropriately in an effort to reduce patient wait times, decrease ED physician burnout rates, and increase the ability of caregivers to provide quality patient care. A prospective is needed to validate the utility of the ANN.
authors.affiliation: Division of Medical Toxicology, Department of Emergency Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA.
authors.forename: Nathan Benjamin
authors.initials: NB
authors.lastname: Menke
authors.affiliation: Department of Emergency Medicine, Lincoln Medical and Mental Health Center, Bronx, NY. Electronic address: ncaputo.md@gmail.com.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Caputo
authors.affiliation: Department of Emergency Medicine, Lincoln Medical and Mental Health Center, Bronx, NY.
authors.forename: Robert
authors.initials: R
authors.lastname: Fraser
authors.affiliation: Department of Emergency Medicine, Lincoln Medical and Mental Health Center, Bronx, NY.
authors.forename: Jordana
authors.initials: J
authors.lastname: Haber
authors.affiliation: Department of Emergency Medicine, Lincoln Medical and Mental Health Center, Bronx, NY.
authors.forename: Christopher
authors.initials: C
authors.lastname: Shields
authors.affiliation: Division of Reproductive, Endocrinology, and Infertility, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh Medical Center, Pittsburgh, PA.
authors.forename: Marie Nam
authors.initials: MN
authors.lastname: Menke
mesh_terms: D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D006801:Humans; D016571:Neural Networks, Computer; D012189:Retrospective Studies; D000078329:Workforce