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Published on: December 11, 2016
An approximate matching method for clinical drug names.
Lee Peters1, Joan E Kapusnik-Uner, Thang Nguyen
1National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA. lpeters@mail.nih.gov
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|December 24, 2011
Summary
This study introduces a drug-centric algorithm for matching local drug names to RxNorm, improving drug terminology mapping. The method effectively resolves variants, aiding drug experts in standardizing formulary data.
Area of Science:
- Pharmacoinformatics
- Medical Informatics
- Computational Linguistics
Background:
- Local drug formularies often contain drug name variants not present in standard terminologies like RxNorm.
- Accurate mapping of these variants to standardized drug concepts is crucial for medication safety and data interoperability.
Purpose of the Study:
- To develop and evaluate an approximate string matching algorithm for identifying the closest RxNorm drug names for variants found in local formularies.
- To enhance the process of mapping local drug names to a standardized reference terminology.
Main Methods:
- A drug-centric algorithm was employed, utilizing token splitting, drug name expansion, and spelling correction for non-exact matches.
- The algorithm was tested against three datasets comprising 17,164 local drug name variants.
Main Results:
- The algorithm achieved mapping rates between 83.8% and 92.8% across the three test sets.
- The correct RxNorm concept was identified as the top candidate in 67.9% to 84.8% of cases and within the top three candidates in 90-96% of cases.
Conclusions:
- The drug-centric token matching approach with aggressive name resolution effectively maps local drug names to RxNorm concepts.
- This method has the potential to significantly assist drug terminology experts in standardizing local formularies against reference terminologies.
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