title: Using UMLS lexical resources to disambiguate abbreviations in clinical text.
abstract: Clinical text is rich in acronyms and abbreviations, and they are highly ambiguous. As a pre-processing step before subsequent NLP analysis, we are developing and evaluating clinical abbreviation disambiguation methods. The evaluation of two sequential steps, the detection and the disambiguation of abbreviations, is reported here, for various types of clinical notes. For abbreviations detection, our result indicated the SPECIALIST Lexicon LRABR needed to be revised for better abbreviation detection. Our semi-supervised method using generated training data based on expanded form matching for 12 frequent abbreviations in our clinical notes reached over 90% accuracy in five-fold cross validation and unsupervised approach produced comparable results with the semi-supervised methods.
authors.affiliation: School of Computing, University of Utah, Salt Lake City, Utah, USA.
authors.forename: Youngjun
authors.initials: Y
authors.lastname: Kim
authors.forename: John
authors.initials: J
authors.lastname: Hurdle
authors.forename: Stéphane M
authors.initials: SM
authors.lastname: Meystre
mesh_terms: D000004:Abbreviations as Topic; D001185:Artificial Intelligence; D008499:Medical Records; D009323:Natural Language Processing; D017432:Unified Medical Language System