A comparison of Bayesian network structure learning algorithms on emergency department ambulance diversion data
Leegon, Jeffrey Thomas
Use of Bayesian networks (BN) has increased in medicine. Traditionally, BNs have been developed by experts or from the current literature. Several applications implement "off the shelf" BN structure learning algorithms, but few implementations have been evaluated. We compared six "off the shelf" BN structure learning algorithms and an expert-developed BN using two years of data from a curated emergency department (ED) overcrowding database. We used ED ambulance diversion as the reference standard. Eighteen variables selected from a previous study were used for prediction. BN structures were learned from a data set for predicting ED diversion one hour in advance. The data set was discretized using equal frequency and equal width discretization. Each BN structure learning algorithm developed a structure based on each data set. We used area under the receiver operating characteristic curve (AUC), negative log likelihood, and Akaike information criterion to compare the structures as they predicted ED diversion at 1, 2, 4, 6, 8, and 12 hours in advance. Both the training and test data sets contained >100,000 data points. The ED was on ambulance diversion 22% of the time. The machine-learned networks were complex, with >3,000 conditional probabilities, compared to the expert-developed network, with 365. Both the best performing machine-learned structure and the expert-developed network had an AUC of 0.95 predicting diversion at one hour and 0.94 predicting diversion at two hours in advance. The machine-learned BN performed as well at the expert-developed BN. The expert-developed network was parsimonious, but required extensive user involvement.