title: FINDING POTENTIALLY UNSAFE NUTRITIONAL SUPPLEMENTS FROM USER REVIEWS WITH TOPIC MODELING.
abstract: Although dietary supplements are widely used and generally are considered safe, some supplements have been identified as causative agents for adverse reactions, some of which may even be fatal. The Food and Drug Administration (FDA) is responsible for monitoring supplements and ensuring that supplements are safe. However, current surveillance protocols are not always effective. Leveraging user-generated textual data, in the form of Amazon.com reviews for nutritional supplements, we use natural language processing techniques to develop a system for the monitoring of dietary supplements. We use topic modeling techniques, specifically a variation of Latent Dirichlet Allocation (LDA), and background knowledge in the form of an adverse reaction dictionary to score products based on their potential danger to the public. Our approach generates topics that semantically capture adverse reactions from a document set consisting of reviews posted by users of specific products, and based on these topics, we propose a scoring mechanism to categorize products as "high potential danger", "average potential danger" and "low potential danger." We evaluate our system by comparing the system categorization with human annotators, and we find that the our system agrees with the annotators 69.4% of the time. With these results, we demonstrate that our methods show promise and that our system represents a proof of concept as a viable low-cost, active approach for dietary supplement monitoring.
authors.affiliation: Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ 85259, USA, rpsulli@asu.edu.
authors.forename: Ryan
authors.initials: R
authors.lastname: Sullivan
authors.forename: Abeed
authors.initials: A
authors.lastname: Sarker
authors.forename: Karen
authors.initials: K
authors.lastname: O'Connor
authors.forename: Amanda
authors.initials: A
authors.lastname: Goodin
authors.forename: Mark
authors.initials: M
authors.lastname: Karlsrud
authors.forename: Graciela
authors.initials: G
authors.lastname: Gonzalez
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D057225:Data Mining / Q000706:statistics & numerical data; D019587:Dietary Supplements / Q000009:adverse effects* / Q000592:standards; D064088:Hazard Analysis and Critical Control Points / Q000379:methods; D006801:Humans; D020407:Internet / Q000706:statistics & numerical data; D000069550:Machine Learning; D015233:Models, Statistical; D009323:Natural Language Processing; D060735:Pharmacovigilance; D062486:Public Health Surveillance / Q000379:methods; D061108:Social Media / Q000706:statistics & numerical data; D014481:United States; D014486:United States Food and Drug Administration