Evidence map›Paper›PMID 23256916›Full record

ArticleJournal of biomedical semantics2012

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

Cui Tao, Yongqun He, Hannah Yang, Gregory A Poland, Christopher G Chute

Abstract read
In one paragraph

Article in Journal of biomedical semantics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  11. OAE: The Ontology of Adverse Events.Journal of biomedical semantics · 2014
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Cui TaoDivision of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. Tao.cui@Mayo.edu.
Yongqun He
Hannah Yang
Gregory A Poland
Christopher G Chute

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelled

backgroundThe 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.

resultsWe 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.

conclusionsWe 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.

Identifiers

PMID23256916
PMCPMC3554604

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.