title: An approximate matching method for clinical drug names.
abstract: OBJECTIVE
  To develop an approximate matching method for finding the closest drug names within existing RxNorm content for drug name variants found in local drug formularies.
  
  
  METHODS
  We used a drug-centric algorithm to determine the closest strings between the RxNorm data set and local variants which failed the exact and normalized string matching searches. Aggressive measures such as token splitting, drug name expansion and spelling correction are used to try and resolve drug names. The algorithm is evaluated against three sets containing a total of 17,164 drug name variants.
  
  
  RESULTS
  Mapping of the local variant drug names to the targeted concept descriptions ranged from 83.8% to 92.8% in three test sets. The algorithm identified the appropriate RxNorm concepts as the top candidate in 76.8%, 67.9% and 84.8% of the cases in the three test sets and among the top three candidates in 90-96% of the cases.
  
  
  CONCLUSIONS
  Using a drug-centric token matching approach with aggressive measures to resolve unknown names provides effective mappings to clinical drug names and has the potential of facilitating the work of drug terminology experts in mapping local formularies to reference terminologies.
authors.affiliation: National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA. lpeters@mail.nih.gov
authors.forename: Lee
authors.initials: L
authors.lastname: Peters
authors.forename: Joan E
authors.initials: JE
authors.lastname: Kapusnik-Uner
authors.forename: Thang
authors.initials: T
authors.lastname: Nguyen
authors.forename: Olivier
authors.initials: O
authors.lastname: Bodenreider
mesh_terms: D000465:Algorithms; D005569:Formularies as Topic; D004364:Pharmaceutical Preparations; D062245:RxNorm; D009626:Terminology as Topic; D017432:Unified Medical Language System
chemical_list: D004364:Pharmaceutical Preparations