The adverse events of the dietary supplements should be subject to scrutiny due to their growing clinical application and consumption among U.S. adults. An effective method for mining and grouping the adverse events of the dietary supplements is to evaluate product labeling for the rapidly increasing number of new products available in the market. In this study, the adverse events information was extracted from the product labels stored in the Dietary Supplement Label Database (DSLD) and analyzed by topic modeling techniques, specifically Latent Dirichlet Allocation (LDA). Among the 50 topics generated by LDA, eight topics were manually evaluated, with topic relatedness ranging from 58.8% to 100% on the product level, and 57.1% to 100% on the ingredient level. Five out of these eight topics were coherent groupings of the dietary supplements based on their adverse events. The results demonstrated that LDA is able to group supplements with similar adverse events based on the dietary supplement labels. Such information can be potentially used by consumers to more safely use dietary supplements.
|Original language||English (US)|
|Title of host publication||MEDINFO 2017|
|Subtitle of host publication||Precision Healthcare through Informatics - Proceedings of the 16th World Congress on Medical and Health Informatics|
|Editors||Zhao Dongsheng, Adi V. Gundlapalli, Jaulent Marie-Christine|
|Number of pages||5|
|State||Published - 2017|
|Event||16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 - Hangzhou, China|
Duration: Aug 21 2017 → Aug 25 2017
|Name||Studies in Health Technology and Informatics|
|Other||16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017|
|Period||8/21/17 → 8/25/17|
Bibliographical noteFunding Information:
Research reported in this publication was supported by the National Center for Complementary & Integrative Health Award (R01AT009457) (Zhang) and the University of Minnesota Grant-In-Aid award (Zhang).
- Dietary supplements
- Natural language processing