Prediction of emergency department visits for respiratory symptoms using an artificial neural network.
OBJECTIVE
Accurate prediction of the effect of atmospheric changes, including pollutants, on emergency department (ED) visits for respiratory symptoms would be useful, but has proven difficult. The main difficulty is the limitation of the classical linear models and logistic regression with multiple variables to handle the multifactorial effect.


METHODS
To predict ED visits, we have created a computer-based model called an artificial neural network (ANN) using a back-propagation training algorithm and genetic algorithm optimization. This ANN was fed meteorologic and air pollution input variables and trained to predict the number of patients admitted to the ED with respiratory symptoms of asthma, COPD, and acute and chronic bronchitis on the corresponding day. One thousand twenty data sets were extracted from an ED admittance database at the Barzilai Medical Center (Ashkelon, Israel), and randomized to a network training set (n = 816) and a test set (n = 204).


RESULTS
The neural network performed best when the predictor variables used were temperature, relative humidity, barometric pressure, SO(2), and oxidation products of nitric oxide, and the data presented as peak value 24 h prior to ED admission and the average during the 7 days before the ED visit. The neural network was able to predict the test set with an average error of 12%.


CONCLUSIONS
Based on meteorologic and pollution data, the use of an ANN can assist in the prediction of ED visits related to respiratory conditions.
Bibi
Haim
H
Barzilai Medical Center, Ashkelon, Israel. haim_76407@yahoo.com
Nutman
Amir
A
Shoseyov
David
D
Shalom
Mendel
M
Peled
Ronit
R
Kivity
Shmuel
S
Nutman
Jacob
J
D000465:Algorithms; D004632:Emergency Medical Services / Q000706:statistics & numerical data*; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008685:Meteorological Concepts; D016571:Neural Networks, Computer; D012120:Respiration Disorders