title: Evaluating semantic relatedness and similarity measures with Standardized MedDRA Queries.
abstract: A potential use of automated concept similarity and relatedness measures is to improve automatic detection of clinical text that relates to a condition indicative of an adverse drug reaction. This is also one of the purposes of the Medical Dictionary for Regulatory Activities (MedDRA) Standardized Queries (SMQ). An expert panel evaluates SMQs for their ability to detect a condition of interest and thus qualifies them as a reference standard for evaluating automated approaches. We compare similarity and relatedness measurement methods on rates of correctly identifying intra-category and inter-category concept pairs from SMQ data to create ROC curves of each method's sensitivity and specificity. Results indicate an information content measure, specifically the Resnik method, achieved the highest results as measured by area under the curve, but using two different measures as predictors, Resnik and Lin, obtained the highest score. Overall, using SMQ data resulted in a productive method of evaluating automated semantic relatedness and similarity scores.
authors.affiliation: Institute for Health Informatics, University of Minnesota, Twin Cities, MN, USA.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Bill
authors.forename: Ying
authors.initials: Y
authors.lastname: Liu
authors.forename: Bridget T
authors.initials: BT
authors.lastname: McInnes
authors.forename: Genevieve B
authors.initials: GB
authors.lastname: Melton
authors.forename: Ted
authors.initials: T
authors.lastname: Pedersen
authors.forename: Serguei
authors.initials: S
authors.lastname: Pakhomov
mesh_terms: D004018:Dictionaries, Medical as Topic; D064420:Drug-Related Side Effects and Adverse Reactions; D016014:Linear Models; D012372:ROC Curve; D012660:Semantics; D017432:Unified Medical Language System