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.
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Acknowledgement
YZ’s effort is supported by the Center for Individualized Medicine at Mayo Clinic.
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Tao, C., Wu, P., Zhang, Y. (2014). Linked Vaccine Adverse Event Data Representation from VAERS for Biomedical Informatics Research. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_58
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DOI: https://doi.org/10.1007/978-3-319-13186-3_58
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