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 language||English (US)|
|Title of host publication||MEDINFO 2019|
|Subtitle of host publication||Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics|
|Editors||Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi|
|Number of pages||5|
|State||Published - Aug 21 2019|
|Event||17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France|
Duration: Aug 25 2019 → Aug 30 2019
|Name||Studies in health technology and informatics|
|Conference||17th World Congress on Medical and Health Informatics, MEDINFO 2019|
|Period||8/25/19 → 8/30/19|
Bibliographical noteFunding Information:
This research was supported by National Center for Complementary & Integrative Health Award (#R01AT009457) (PI: Zhang). The content is solely the responsibility of the authors and does not represent the official views of the National Center for Complementary & Integrative Health. This work was also supported by the Intramural Research Program of the NIH.
© 2019 International Medical Informatics Association (IMIA) and IOS Press.
- Dietary supplements
- Natural Language Processing
- Dietary Supplements
- Databases, Factual
PubMed: MeSH publication types
- Journal Article