Work

Normalizing Dietary Supplement Product Names Using the RxNorm Model

Year: 2019

Type: book-chapter

Abstract: 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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti190253

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

Institutions: University of Minnesota, National Institutes of Health, United States National Library of Medicine

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: National Center for Complementary and Integrative Health