Normalizing dietary supplement product names using the RxNorM model

Jake Vasilakes, Yadan Fan, Rubina Rizvi, Anusha Bompelli, Olivier Bodenreider, Rui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The use of dietary supplements (DSs) is increasing in the U.S. As such, it is crucial for consumers, clinicians, and researchers to be able to find information about DS products. However, labeling regulations allow great variability in DS product names, which makes searching for this information difficult. Following the RxNorm drug name normalization model, we developed a rule-based natural language processing system to normalize DS product names using pattern templates. We evaluated the system on product names extracted from the Dietary Supplement Label Database. Our system generated 136 unique templates and obtained a coverage of 72%, a 32% increase over the existing RxNorm model. Manual review showed that our system achieved a normalization accuracy of 0.86. We found that the normalization of DS product names is feasible, but more work is required to improve the generalizability of the system.

Original languageEnglish (US)
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Number of pages5
ISBN (Electronic)9781643680026
StatePublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019


  • Dietary supplements
  • Natural Language Processing
  • RxNorm

PubMed: MeSH publication types

  • Journal Article

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