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Author Menke, Nathan Benjamin ♦ Caputo, Nicholas ♦ Fraser, Robert ♦ Haber, Jordana ♦ Shields, Christopher ♦ Menke, Marie Nam
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Elsevier
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Social sciences ♦ Social problems & services; associations ♦ Social welfare problems & services ♦ Natural sciences & mathematics ♦ Mathematics ♦ Life sciences; biology ♦ Physiology & related subjects ♦ Natural history of organisms ♦ Technology ♦ Medicine & health ♦ Human physiology ♦ Diseases ♦ Manufacture for specific uses ♦ Precision instruments & other devices
Subject Domain (in MeSH) Eukaryota ♦ Organisms ♦ Investigative Techniques ♦ Analytical, Diagnostic and Therapeutic Techniques and Equipment ♦ Mathematical Concepts ♦ Biological Sciences ♦ Health Care Facilities, Manpower, and Services ♦ Health Care
Subject Keyword Discipline Emergency ♦ Discipline Medicine ♦ Emergency Service, Hospital ♦ Statistics & Numerical Data ♦ Neural Networks (computer) ♦ Manpower ♦ Humans ♦ Retrospective Studies ♦ Journal Article
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. CONCLUSION: 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.
Description Country affiliation: Panama
Author Affiliation: Menke NB ( Division of Medical Toxicology, Department of Emergency Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA.); Caputo N ( Department of Emergency Medicine, Lincoln Medical and Mental Health Center, Bronx, NY. Electronic address: ncaputo.md@gmail.com.); Fraser R ( Department of Emergency Medicine, Lincoln Medical and Mental Health Center, Bronx, NY.); Haber J ( Department of Emergency Medicine, Lincoln Medical and Mental Health Center, Bronx, NY.); Shields C ( Department of Emergency Medicine, Lincoln Medical and Mental Health Center, Bronx, NY.); Menke MN ( Division of Reproductive, Endocrinology, and Infertility, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh Medical Center, Pittsburgh, PA.)
ISSN 07356757
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-06-01
Publisher Place United States
e-ISSN 15328171
Journal The American Journal of Emergency Medicine
Volume Number 32
Issue Number 6


Source: WHO-Global Index Medicus