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Normalizing Dietary Supplement Product Names Using the RxNorm Model.

Jake Vasilakes1,2, Yadan Fan1, Rubina Rizvi1,2

  • 1Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.

Studies in Health Technology and Informatics
|August 24, 2019
PubMed
Summary
This summary is machine-generated.

This study developed a natural language processing system to normalize dietary supplement (DS) product names, improving information retrieval for consumers and researchers. The system achieved 72% coverage and 0.86 accuracy, demonstrating the feasibility of DS name normalization.

Keywords:
Dietary supplementsNatural Language ProcessingRxNorm

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Area of Science:

  • Pharmacology
  • Natural Language Processing
  • Health Informatics

Background:

  • Dietary supplement (DS) use is rising, necessitating better access to product information.
  • Inconsistent DS product naming conventions hinder effective searching and data analysis.
  • Existing drug name normalization models do not fully address DS nomenclature challenges.

Purpose of the Study:

  • To develop and evaluate a rule-based natural language processing system for normalizing dietary supplement product names.
  • To improve the discoverability and standardization of information related to dietary supplements.
  • To adapt and enhance the RxNorm drug name normalization model for dietary supplements.

Main Methods:

  • Developed a rule-based natural language processing system using pattern templates for DS name normalization.
  • Utilized product names from the Dietary Supplement Label Database for system evaluation.
  • Applied a methodology inspired by the RxNorm drug name normalization model.

Main Results:

  • Generated 136 unique normalization templates.
  • Achieved 72% coverage of DS product names, a 32% improvement over the RxNorm model.
  • Demonstrated a manual review normalization accuracy of 0.86.

Conclusions:

  • Normalization of dietary supplement product names is feasible and beneficial.
  • The developed system significantly improves upon existing models for DS name standardization.
  • Further research is needed to enhance the generalizability of the normalization system.