Post-marketing Drug Safety Evaluation Using Data Mining Based on FAERS

  • Rui Duan
  • Xinyuan Zhang
  • Jingcheng Du
  • Jing Huang
  • Cui TaoEmail author
  • Yong ChenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10387)


Healthcare is going through a big data revolution. The amount of data generated by healthcare is expected to increase significantly in the coming years. Therefore, efficient and effective data processing methods are required to transform data into information. In addition, applying statistical analysis can transform the information into useful knowledge. We developed a data mining method that can uncover new knowledge in this enormous field for clinical decision making while generating scientific methods and hypotheses. The proposed pipeline can be generally applied to a variety of data mining tasks in medical informatics. For this study, we applied the proposed pipeline for post-marketing surveillance on drug safety using FAERS, the data warehouse created by FDA. We used 14 kinds of neurology drugs to illustrate our methods. Our result indicated that this approach can successfully reveal insight for further drug safety evaluation.


Data mining Post-marketing surveillance Zero-truncated negative binomial regression model 



This research was partially supported by the National Library of Medicine of the National Institutes of Health under Award Number R01LM011829, the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number R01AI130460, and the UTHealth Innovation for Cancer Prevention Research Training Program Pre-doctoral Fellowship (Cancer Prevention and Research Institute of Texas grant # RP160015).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of PennsylvaniaPhiladelphiaUSA
  2. 2.University of Texas Health Science Center at HoustonHoustonUSA

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