title: Learning and discovery from a clinical database: an incremental concept formation approach.
abstract: The main interest of this research is to discover clinical implications from a large PTCA (Percutaneous Transluminal Coronary Angioplasty) database. A case-based concept formation model D-UNIMEM, a modified version of Lebowitz's UNIMEM, is proposed for this purpose. In this model, we integrated two kinds of class memberships: the feature-disjunction class membership and the index-conjunction class membership. The former is a polythetic clustering approach and serves at the early stage of concept formation. The latter allows only relevant instances to be placed in the same cluster and serves as the later stage of concept formation. D-UNIMEM could extract interesting correlations among features from the learned concept hierarchy.
authors.affiliation: Department of Computer Science, National Tsing-Hua University, Hsin-Chu, Taiwan.
authors.forename: V W
authors.initials: VW
authors.lastname: Soo
authors.forename: J S
authors.initials: JS
authors.lastname: Wang
authors.forename: S P
authors.initials: SP
authors.lastname: Wang
mesh_terms: D000043:Abstracting and Indexing; D000787:Angina Pectoris; D015906:Angioplasty, Balloon, Coronary / Q000009:adverse effects; D001185:Artificial Intelligence; D006328:Cardiac Catheterization; D001026:Coronary Artery Bypass; D003920:Diabetes Mellitus; D004562:Electrocardiography; D005080:Exercise Test; D015637:Gated Blood-Pool Imaging; D006801:Humans; D006973:Hypertension; D007256:Information Systems; D007858:Learning; D016571:Neural Networks, Computer; D012307:Risk Factors; D012907:Smoking