A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources

Sungrim Moon, Serguei Pakhomov, Nathan Liu, James O. Ryan, Genevieve B. Melton

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

Objective: To create a sense inventory of abbreviations and acronyms from clinical texts. Methods: The most frequently occurring abbreviations and acronyms from 352 267 dictated clinical notes were used to create a clinical sense inventory. Senses of each abbreviation and acronym were manually annotated from 500 random instances and lexically matched with long forms within the Unified Medical Language System (UMLS V.2011AB), Another Database of Abbreviations in Medline (ADAM), and Stedman's Dictionary, Medical Abbreviations, Acronyms & Symbols, 4th edition (Stedman's). Redundant long forms were merged after they were lexically normalized using Lexical Variant Generation (LVG). Results: The clinical sense inventory was found to have skewed sense distributions, practice-specific senses, and incorrect uses. Of 440 abbreviations and acronyms analyzed in this study, 949 long forms were identified in clinical notes. This set was mapped to 17 359, 5233, and 4879 long forms in UMLS, ADAM, and Stedman's, respectively. After merging long forms, only 2.3% matched across all medical resources. The UMLS, ADAM, and Stedman's covered 5.7%, 8.4%, and 11% of the merged clinical long forms, respectively. The sense inventory of clinical abbreviations and acronyms and anonymized datasets generated from this study are available for public use at http://www.bmhi.umn.edu/ihi/ research/nlpie/resources/index.htm ('Sense Inventories', website). Conclusions: Clinical sense inventories of abbreviations and acronyms created using clinical notes and medical dictionary resources demonstrate challenges with term coverage and resource integration. Further work is needed to help with standardizing abbreviations and acronyms in clinical care and biomedicine to facilitate automated processes such as text-mining and information extraction.

Original languageEnglish (US)
Pages (from-to)299-307
Number of pages9
JournalJournal of the American Medical Informatics Association
Volume21
Issue number2
DOIs
StatePublished - Feb 21 2014

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