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Open Access Research

Ontology-based time information representation of vaccine adverse events in VAERS for temporal analysis

Cui Tao1*, Yongqun He2, Hannah Yang1, Gregory A Poland3 and Christopher G Chute1

Author Affiliations

1 Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

2 Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA

3 Mayo Vaccine Research Group, Mayo Clinic, Rochester, MN, USA

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Journal of Biomedical Semantics 2012, 3:13  doi:10.1186/2041-1480-3-13

Published: 20 December 2012

Abstract

Background

The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) provides a valuable data source for post-vaccination adverse event analyses. The structured data in the system has been widely used, but the information in the write-up narratives is rarely included in these kinds of analyses. In fact, the unstructured nature of the narratives makes the data embedded in them difficult to be used for any further studies.

Results

We developed an ontology-based approach to represent the data in the narratives in a “machine-understandable” way, so that it can be easily queried and further analyzed. Our focus is the time aspect in the data for time trending analysis. The Time Event Ontology (TEO), Ontology of Adverse Events (OAE), and Vaccine Ontology (VO) are leveraged for the semantic representation of this purpose. A VAERS case report is presented as a use case for the ontological representations. The advantages of using our ontology-based Semantic web representation and data analysis are emphasized.

Conclusions

We believe that representing both the structured data and the data from write-up narratives in an integrated, unified, and “machine-understandable” way can improve research for vaccine safety analyses, causality assessments, and retrospective studies.