title: A Bayesian network for mammography.
abstract: The interpretation of a mammogram and decisions based on it involve reasoning and management of uncertainty. The wide variation of training and practice among radiologists results in significant variability in screening performance with attendant cost and efficacy consequences. We have created a Bayesian belief network to integrate the findings on a mammogram, based on the standardized lexicon developed for mammography, the Breast Imaging Reporting And Data System (BI-RADS). Our goal in creating this network is to explore the probabilistic underpinnings of this lexicon as well as standardize mammographic decision-making to the level of expert knowledge.
authors.affiliation: Stanford Medical Informatics, Stanford University, Stanford, CA, USA.
authors.forename: E
authors.initials: E
authors.lastname: Burnside
authors.forename: D
authors.initials: D
authors.lastname: Rubin
authors.forename: R
authors.initials: R
authors.lastname: Shachter
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001941:Breast Diseases / Q000000981:diagnostic imaging; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005103:Expert Systems; D005260:Female; D006801:Humans; D020407:Internet; D008327:Mammography / Q000592:standards*; D008875:Middle Aged; D011857:Radiographic Image Interpretation, Computer-Assisted