Linked vaccine adverse event data representation from VAERS for biomedical informatics research

Cui Tao, Puqiang Wu, Yuji Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, also comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more 200,000 reports for post-vaccination events that occur after the administration of vaccines licensed in the United States. Although the data from VAERS has been applied to many public health and vaccine safety studies, each individual report does not necessary indicate a casuality relationship between the vaccine and the reported symptoms. Further statistical analysis and summarization needs to be done before this data can be leveraged. In this paper, we introduces our preliminary work on summarzing the VAERS data and representing the vaccine-symptom correlations as well as the meta data of their relations using RDF. We then apply network analysis approaches to the RDF data to illustrate a use case of the data. We further discuss our vision on integrating the data with vaccine information from other sources using RDF linked approach to faciliate more comprehensive analyses.

Original languageEnglish (US)
Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops
Subtitle of host publicationDANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers
EditorsWen-Chih Peng, Haixun Wang, Zhi-Hua Zhou, Tu Bao Ho, Vincent S. Tseng, Arbee L.P. Chen, James Bailey
PublisherSpringer Verlag
Pages652-661
Number of pages10
ISBN (Electronic)9783319131856
DOIs
StatePublished - Jan 1 2014
EventInternational Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China
Duration: May 13 2014May 16 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8643
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014
CountryTaiwan, Province of China
CityTainan
Period5/13/145/16/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tao, C., Wu, P., & Zhang, Y. (2014). Linked vaccine adverse event data representation from VAERS for biomedical informatics research. In W-C. Peng, H. Wang, Z-H. Zhou, T. B. Ho, V. S. Tseng, A. L. P. Chen, & J. Bailey (Eds.), Trends and Applications in Knowledge Discovery and Data Mining - PAKDD 2014 International Workshops: DANTH, BDM, MobiSocial, BigEC, CloudSD, MSMV-MBI, SDA, DMDA-Health, ALSIP, SocNet, DMBIH, BigPMA, Revised Selected Papers (pp. 652-661). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8643). Springer Verlag. https://doi.org/10.1007/978-3-319-13186-3_58