title: Using SemRep to label semantic relations extracted from clinical text.
abstract: In this paper we examined the relationship between semantic relatedness among medical concepts found in clinical reports and biomedical literature. Our objective is to determine whether relations between medical concepts identified from Medline abstracts may be used to inform us as to the nature of the association between medical concepts that appear to be closely related based on their distribution in clinical reports. We used a corpus of 800k inpatient clinical notes as a source of data for determining the strength of association between medical concepts and SemRep database as a source of labeled relations extracted from Medline abstracts. The same pair of medical concepts may be found with more than one predicate type in the SemRep database but often with different frequencies. Our analysis shows that predicate type frequency information obtained from the SemRep database appears to be helpful for labeling semantic relations obtained with measures of semantic relatedness and similarity.
authors.affiliation: University of Minnesota, Minneapolis, MN, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Liu
authors.forename: Robert
authors.initials: R
authors.lastname: Bill
authors.forename: Marcelo
authors.initials: M
authors.lastname: Fiszman
authors.forename: Thomas
authors.initials: T
authors.lastname: Rindflesch
authors.forename: Ted
authors.initials: T
authors.lastname: Pedersen
authors.forename: Genevieve B
authors.initials: GB
authors.lastname: Melton
authors.forename: Serguei V
authors.initials: SV
authors.lastname: Pakhomov
mesh_terms: D000043:Abstracting and Indexing; D019992:Databases as Topic; D006801:Humans; D016247:Information Storage and Retrieval; D016239:MEDLINE; D008499:Medical Records; D009323:Natural Language Processing; D012660:Semantics; D012984:Software; D009626:Terminology as Topic; D017432:Unified Medical Language System