Evaluating semantic relatedness and similarity measures with Standardized MedDRA Queries.

Robert W. Bill, Ying Liu, Bridget T. McInnes, Genevieve B Melton-Meaux, Ted Pedersen, Serguei V Pakhomov

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)43-50
Number of pages8
JournalUnknown Journal
Volume2012
StatePublished - 2012

Fingerprint Dive into the research topics of 'Evaluating semantic relatedness and similarity measures with Standardized MedDRA Queries.'. Together they form a unique fingerprint.

Cite this