title: Combining contextual and lexical features to classify UMLS concepts.
abstract: Semantic classification is important for biomedical terminologies and the many applications that depend on them. Previously we developed two classifiers for 8 broad clinically relevant classes to reclassify and validate UMLS concepts. We found them to be complementary, and then combined them using a manual approach. In this paper, we extended the classifiers by adding an "other" class to categorize concepts not belonging to any of the 8 classes. In addition, we focused on automating the method for combining the two classifiers by training a meta-classifier that performs dynamic combination to exploit the strength of each classifier. The automated method performed as well as manual combination, achieving classification accuracy of about 0.81.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Jung-Wei
authors.initials: JW
authors.lastname: Fan
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
mesh_terms: D035843:Biomedical Research / Q000145:classification; D008490:Medical Informatics / Q000145:classification; D012660:Semantics; D009626:Terminology as Topic; D017432:Unified Medical Language System / Q000145:classification*