Comparing Decision Support Methodologies for Identifying Asthma Exacerbations

OBJECTIVE To apply and compare common machine learning techniques with an expert-built Bayesian Network to determine eligibility for asthma guidelines in pediatric emergency department patients. POPULATION All patients 2-18 years of age presenting to a pediatric emergency department during a 2-month study period. METHODS We created an artificial neural network, a support vector machine, a Gaussian process, and a learned Bayesian network to compare each method's ability to detect patients eligible for asthma guidelines. Our outcome measures included the area under the receiver operating characteristic curves, sensitivity, specificity, predictive values, and likelihood ratios. RESULTS The data were randomly split into a training set (n=3017) and test set (n=1006) for analysis. The systems performed equally well. The area under the receiver operating characteristic curve was 0.959 for the expert-built Bayesian network, 0.962 for the automatically constructed Bayesian network, 0.956 for the Gaussian Process, and 0.937 for the artificial neural network. DISCUSSION All four evaluated machine learning methods achieved high accuracy. The automatically created Bayesian network performed similarly to the expert-built network. These methods could be applied to create a realtime detection system for identifying asthma patients.