title: Synonym, topic model and predicate-based query expansion for retrieving clinical documents.
abstract: We present a study that developed and tested three query expansion methods for the retrieval of clinical documents. Finding relevant documents in a large clinical data warehouse is a challenging task. To address this issue, first, we implemented a synonym expansion strategy that used a few selected vocabularies. Second, we trained a topic model on a large set of clinical documents, which was then used to identify related terms for query expansion. Third, we obtained related terms from a large predicate database derived from Medline abstracts for query expansion. The three expansion methods were tested on a set of clinical notes. All three methods successfully achieved higher average recalls and average F-measures when compared with the baseline method. The average precisions and precision at 10, however, decreased with all expansions. Amongst the three expansion methods, the topic model-based method performed the best in terms of recall and F-measure.
authors.affiliation: VA Salt Lake City, UT, USA.
authors.forename: Qing T
authors.initials: QT
authors.lastname: Zeng
authors.forename: Doug
authors.initials: D
authors.lastname: Redd
authors.forename: Thomas
authors.initials: T
authors.lastname: Rindflesch
authors.forename: Jonathan
authors.initials: J
authors.lastname: Nebeker
mesh_terms: D000043:Abstracting and Indexing; D015510:Clinical Medicine; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D008499:Medical Records; D009323:Natural Language Processing; D017432:Unified Medical Language System; D018875:Vocabulary, Controlled