A retrospective analysis of the utility of an artificial neural network to predict ED volume

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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.

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