| アブストラクト | BACKGROUND: Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, sometimes comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 200,000 reports for post-vaccination events that occur after the administration of vaccines licensed in the United States. Although the data from the VAERS has been applied to many public health and vaccine safety studies, each individual report does not necessarily 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. METHODS: This paper introduces our efforts on representing the vaccine-symptom correlations and their corresponding meta-information extracted from the VAERS database using Resource Description Framework (RDF). Numbers of occurrences of vaccine-symptom pairs reported to the VAERS were summarized with corresponding proportional reporting ratios (PRR) calculated. All the data was stored in an RDF file. We then applied network analysis approaches to the RDF data to illustrate a use case of the data for longititual studies. We further dicussed our vision on integrating the data with vaccine information from other sources using RDF linked approach to facilitate more comprehensive analyses. RESULTS: The 1990-2013 data from VAERS has been extracted from the VAERS database. There are 83,148 unique vaccine-symptom pairs with 75 vaccine types and 5,865 different reported symptoms. The yearly and over PRR values for each reported vaccine-symptom pair were calculated. The network properties of networks consisting of significant vaccine-symptom associations (i.e., PRR larger than 1) were then investigated. The results indicated that vaccine-symptom association network is a dense network, with any given node connected to all other nodes through an average of approximately two other nodes and a maximum of five nodes. |
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| ジャーナル名 | BioData mining |
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| 投稿日 | 2015/2/24 |
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| 投稿者 | Tao, Cui; Wu, Puqiang; Luo, Yi; Zhang, Yuji |
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| 組織名 | School of Biomedical Informatics, The University of Texas Health Science Center;at Houston, Houston, TX USA.;University of Wisconsin-Madison, Madison, WI USA.;at Houston, Houston, TX USA ; Department of Computer Science and Engineering,;Lehigh University, Bethlehem, PA USA.;Division of Biostatistics and Bioinformatics, University of Maryland Greenebaum;Cancer Center, Baltimore, MD USA ; Department of Epidemiology and Public Health,;University of Maryland School of Medicine, Baltimore, MD USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/25699091/ |
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