ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2016
FINDING POTENTIALLY UNSAFE NUTRITIONAL SUPPLEMENTS FROM USER REVIEWS WITH TOPIC MODELING.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Using natural language processing to characterize and predict homeopathic product-associated adverse events in consumer reviews: comparison to reports to FDA Adverse Event Reporting System (FAERS).Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Serious Skin Injuries Following Exposure to Unapproved Mole and Skin Tag Removers.The Journal of clinical and aesthetic dermatology · 2023Article
- The Evaluation of Videos about Branched-Chain Amino Acids Supplements on YouTubeInternational journal of environmental research and public health · 2022Article
- Social media analytics in nutrition research: a rapid review of current usage in investigation of dietary behaviours.Public health nutrition · 2021Review
- Patient Triage by Topic Modeling of Referral Letters: Feasibility Study.JMIR medical informatics · 2020Article
- Mining Social Media Data for Biomedical Signals and Health-Related Behavior.Annual review of biomedical data science · 2020Article
- iDISK: the integrated DIetary Supplements Knowledge base.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Article
- Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach.Journal of medical Internet research · 2018Article
- Biomedical Text Categorization Based on Ensemble Pruning and Optimized Topic Modelling.Computational and mathematical methods in medicine · 2018Article
- Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.Yearbook of medical informatics · 2017Review
- Mining Adverse Events of Dietary Supplements from Product Labels by Topic Modeling.Studies in health technology and informatics · 2017Article
- Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.Biomedical informatics insights · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
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.
Indexed as
Identifiers
26776215PMC6391886What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.