title: Knowledge discovery from structured mammography reports using inductive logic programming.
abstract: The development of large mammography databases provides an opportunity for knowledge discovery and data mining techniques to recognize patterns not previously appreciated. Using a database from a breast imaging practice containing patient risk factors, imaging findings, and biopsy results, we tested whether inductive logic programming (ILP) could discover interesting hypotheses that could subsequently be tested and validated. The ILP algorithm discovered two hypotheses from the data that were 1) judged as interesting by a subspecialty trained mammographer and 2) validated by analysis of the data itself.
authors.affiliation: University of Wisconsin.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
authors.forename: Jesse
authors.initials: J
authors.lastname: Davis
authors.forename: Victor Santos
authors.initials: VS
authors.lastname: Costa
authors.forename: Inês de Castro
authors.initials: Ide C
authors.lastname: Dutra
authors.forename: Charles E
authors.initials: CE
authors.lastname: Kahn
authors.forename: Jason
authors.initials: J
authors.lastname: Fine
authors.forename: David
authors.initials: D
authors.lastname: Page
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001706:Biopsy; D001941:Breast Diseases / Q000000981:diagnostic imaging; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005240:Feasibility Studies; D006801:Humans; D016247:Information Storage and Retrieval; D008128:Logic; D008327:Mammography; D011237:Predictive Value of Tests; D012984:Software