A Bayesian network model for radiological diagnosis and procedure selection: work-up of suspected gallbladder disease.
Bayesian networks, a technique for reasoning under uncertainty, currently are being developed for application to medical decision making. To explore their usefulness for radiologic decision support, a Bayesian belief network was constructed in the domain of hepatobiliary disease. The network model's nodes represent diagnoses, physical findings, laboratory test results, and imaging study findings. The connections between nodes incorporate conditional probabilities, such as sensitivity and specificity, to represent probabilistic influences. Statistical data were abstracted from peer-reviewed journal articles on hepatobiliary disease, and a network was created to reflect the data. The network successfully determined the a priori probabilities of various diseases, and incorporated laboratory and imaging results to calculate the a posteriori probabilities. The most informative examination was identified, that is, the laboratory study or imaging procedure that led to the greatest diagnostic certainty. Bayesian networks represent a very promising technique for decision support in radiology: they can assist physicians in formulating diagnoses and in selecting imaging procedures.
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee 53201.
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Haddawy
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Kahn
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Butarbutar
D000328:Adult; D001499:Bayes Theorem; D002764:Cholecystitis / Q000175:diagnosis; D002769:Cholelithiasis / Q000175:diagnosis; D003661:Decision Support Techniques; D005260:Female; D005705:Gallbladder Diseases / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D006801:Humans; D008297:Male; D011859:Radiography