title: Detecting abbreviations in discharge summaries using machine learning methods.
abstract: Recognition and identification of abbreviations is an important, challenging task in clinical natural language processing (NLP). A comprehensive lexical resource comprised of all common, useful clinical abbreviations would have great applicability. The authors present a corpus-based method to create a lexical resource of clinical abbreviations using machine-learning (ML) methods, and tested its ability to automatically detect abbreviations from hospital discharge summaries. Domain experts manually annotated abbreviations in seventy discharge summaries, which were randomly broken into a training set (40 documents) and a test set (30 documents). We implemented and evaluated several ML algorithms using the training set and a list of pre-defined features. The subsequent evaluation using the test set showed that the Random Forest classifier had the highest F-measure of 94.8% (precision 98.8% and recall of 91.2%). When a voting scheme was used to combine output from various ML classifiers, the system achieved the highest F-measure of 95.7%.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
authors.forename: Yonghui
authors.initials: Y
authors.lastname: Wu
authors.forename: S Trent
authors.initials: ST
authors.lastname: Rosenbloom
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Denny
authors.forename: Randolph A
authors.initials: RA
authors.lastname: Miller
authors.forename: Subramani
authors.initials: S
authors.lastname: Mani
authors.forename: Dario A
authors.initials: DA
authors.lastname: Giuse
authors.forename: Hua
authors.initials: H
authors.lastname: Xu
mesh_terms: D000004:Abbreviations as Topic; D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D010351:Patient Discharge; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine