title: Discovery of association rules in medical data.
abstract: Data mining is a technique for discovering useful information from large databases. This technique is currently being profitably used by a number of industries. A common approach for information discovery is to identify association rules which reveal relationships among different items. In this paper, we use this approach to analyse a large database containing medical-record data. Our aim is to obtain association rules indicating relationships between procedures performed on a patient and the reported diagnoses. Random sampling was used to obtain these association rules. After reviewing the basic concepts associated with data mining, we discuss our approach for identifying association rules and report on the rules generated.
authors.affiliation: Los Alamos National Laboratory, NM 87545, USA.
authors.forename: S
authors.initials: S
authors.lastname: Doddi
authors.forename: A
authors.initials: A
authors.lastname: Marathe
authors.forename: S S
authors.initials: SS
authors.lastname: Ravi
authors.forename: D C
authors.initials: DC
authors.lastname: Torney
mesh_terms: D000465:Algorithms; D016001:Confidence Intervals; D016208:Databases, Factual; D003953:Diagnosis-Related Groups; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008491:Medical Informatics Applications; D008492:Medical Informatics Computing; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D011897:Random Allocation