Mining Adverse Events of Dietary Supplements from Product Labels by Topic Modeling

摘要：

　　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.

关键词： Dietary Supplements Natural Language Processing Pharmacovigilance

作者: Yefeng Wang Divya R.Gunashekar Terrence J.Adam Rui Zhang

作者单位: Institute for Health Informatics,University of Minnesota,Minneapolis,MN,USA School of Public Health,University of Minnesota,Minneapolis,MN,USA Institute for Health Informatics,University of Minnesota,Minneapolis,MN,USA;College of Pharmacy,Univ Institute for Health Informatics,University of Minnesota,Minneapolis,MN,USA;Department of Surgery,Un

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 614-618

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）