Literature DB >> 31258978

Detecting Signals of Dietary Supplement Adverse Events from the CFSAN Adverse Event Reporting System (CAERS).

Jake A Vasilakes1,2, Rubina F Rizvi1,2, Jianqiu Zhang1, Terrence J Adam1,2, Rui Zhang1,2.   

Abstract

Dietary supplement adverse events are potentially severe, yet knowledge regarding the safety of dietary supplements is limited. The CFSAN Adverse Event Reporting System (CAERS) contains records of adverse events attributed to supplements and is potentially useful for dietary supplement pharmacovigilance. This study investigates the feasibility of mining CAERS for dietary supplement adverse events as well as for monitoring the safety of dietary supplement products. Using three online resources, we mapped products in CAERS to their listed ingredients. We then ran four standard signal detection algorithms over the ingredient-adverse event and product-adverse event pairs extracted from CAERS and ranked the detected associations. Comparing 130 signals detected by all four algorithms with a dietary supplement resource, we found evidence for 73 (56%) associations. In addition, some detected product-adverse event signals were consistent with product safety information. We have made a database of the detected adverse events publicly available at https://github.com/zhang-informatics/DDSAE.

Year:  2019        PMID: 31258978      PMCID: PMC6568094     

Source DB:  PubMed          Journal:  AMIA Jt Summits Transl Sci Proc


  3 in total

1.  Assessing the enrichment of dietary supplement coverage in the Unified Medical Language System.

Authors:  Jake Vasilakes; Anusha Bompelli; Jeffrey R Bishop; Terrence J Adam; Olivier Bodenreider; Rui Zhang
Journal:  J Am Med Inform Assoc       Date:  2020-10-01       Impact factor: 4.497

2.  Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.

Authors:  Yadan Fan; Sicheng Zhou; Yifan Li; Rui Zhang
Journal:  J Am Med Inform Assoc       Date:  2021-03-01       Impact factor: 4.497

3.  Normalizing Dietary Supplement Product Names Using the RxNorm Model.

Authors:  Jake Vasilakes; Yadan Fan; Rubina Rizvi; Anusha Bompelli; Olivier Bodenreider; Rui Zhang
Journal:  Stud Health Technol Inform       Date:  2019-08-21
  3 in total

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