An artificial neural network ensemble to predict disposition and length of stay in children presenting with bronchiolitis.
BACKGROUND
Artificial neural networks apply complex non-linear functions to pattern recognition problems. An ensemble is a 'committee' of neural networks that usually outperforms single neural networks. Bronchiolitis is a common manifestation of viral lower respiratory tract infection in infants and toddlers.


OBJECTIVE
To train artificial neural network ensembles to predict the disposition and length of stay in children presenting to the Emergency Department with bronchiolitis.


METHODS
A specifically constructed database of 119 episodes of bronchiolitis was used to train, validate, and test a neural network ensemble. We used EasyNN 7.0 on a 200 Mhz pentium PC with a maths co-processor. The ensemble of neural networks constructed was subjected to fivefold validation. Comparison with actual and predicted dispositions was measured using the kappa statistic for disposition and the Kaplan-Meier estimations and log rank test for predictions of length of stay.


RESULTS
The neural network ensembles correctly predicted disposition in 81% (range 75-90%) of test cases. When compared with actual disposition the neural network performed similarly to a logistic regression model and significantly better than various 'dumb machine' strategies with which we compared it. The prediction of length of stay was poorer, 65% (range 60-80%), but the difference between observed and predicted lengths of stay were not significantly different.


CONCLUSIONS
Artificial neural network ensembles can predict disposition for infants and toddlers with bronchiolitis; however, the prediction of length of hospital stay is not as good.
Walsh
Paul
P
Department of Emergency Medicine, Kern Medical Center, Bakersfield, CA 93305, USA. yousentwhohome@netscape.net
Cunningham
Padraig
P
Rothenberg
Stephen J
SJ
O'Doherty
Sinead
S
Hoey
Hilary
H
Healy
Roisin
R
D001988:Bronchiolitis / Q000175:diagnosis* / Q000453:epidemiology* / Q000628:therapy; D002648:Child; D002673:Child, Hospitalized / Q000706:statistics & numerical data; D002675:Child, Preschool; D004636:Emergency Service, Hospital; D005260:Female; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D011379:Prognosis; D012042:Registries; D012044:Regression Analysis; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D016896:Treatment Outcome