Prospective evaluation of a Bayesian Network for detecting asthma exacerbations in a Pediatric Emergency Department.
Early detection of asthma exacerbations may allow for automated guideline enrollment. We developed and prospectively evaluated a real-time Bayesian network to predict the presence of acute asthma after patient triage using only routinely available electronic data. 2,006 patients were enrolled, including 153 guideline-eligible patients. The area under the ROC curve was 0.971 (95% CI: 0.955 to 0.981) for identifying asthma patients. The system can be applied to identify guideline eligible patients and remind clinicians to enroll patients into guidelines.
Sanders
David L
DL
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.
Aronsky
Dominik
D
D000208:Acute Disease; D000293:Adolescent; D001249:Asthma / Q000175:diagnosis*; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D003936:Diagnosis, Computer-Assisted; D004636:Emergency Service, Hospital; D006801:Humans; D016571:Neural Networks, Computer; D011446:Prospective Studies; D012372:ROC Curve; D012680:Sensitivity and Specificity