OBJECTIVE: To develop and evaluate a Bayesian network to identify @entity1 eligible for an @entity565 -care guideline using only data available electronically at the time of @entity1 triage. POPULATION: Consecutive @entity1 2-18 years old who presented to a pediatric emergency department during a 2-month period. METHODS: A network was developed and evaluated using clinical data from @entity1 visits. An independent reference standard for @entity565 guideline eligibility was established and verified for each @entity1 through chart review. Outcome measures were area under the receiver operating characteristic curve, sensitivity, specificity, predictive values, and likelihood ratios. RESULTS: We enrolled 3,023 @entity1 visits, including 385 who were eligible for guideline-based care. Area under the receiver operating curve for the network was 0.959 (95% CI = 0.933 - 0.977). At a fixed 90% sensitivity, specificity was 88.3%, positive predictive value was 44.7% and negative predictive value was 98.8%. The positive likelihood ratio was 7.69 and the negative likelihood ratio was 0.11. CONCLUSION: The Bayesian network was able to detect @entity1 eligible for an @entity565 guideline with high accuracy suggesting that this technique could be used to automatically initiate guideline use for eligible @entity1 .

Detecting XXXX exacerbations in a pediatric emergency department using a Bayesian network.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity565 :: ('MESH:D001249', 'Disease') :: ['asthma']
@entity565 :: (MESH:D001249,Disease) :: ['asthma']
