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. 2012;7(5):e36759.
doi: 10.1371/journal.pone.0036759. Epub 2012 May 14.

Center of excellence in research reporting in neurosurgery--diagnostic ontology

Amrapali Zaveri  1 , Jatin Shah, Shreyasee Pradhan, Clarissa Rodrigues, Jacson Barros, Beng Ti Ang, Ricardo Pietrobon
Affiliations

Center of excellence in research reporting in neurosurgery--diagnostic ontology

Amrapali Zaveri et al. PLoS One. 2012.

Abstract

Motivation: Evidence-based medicine (EBM), in the field of neurosurgery, relies on diagnostic studies since Randomized Controlled Trials (RCTs) are uncommon. However, diagnostic study reporting is less standardized which increases the difficulty in reliably aggregating results. Although there have been several initiatives to standardize reporting, they have shown to be sub-optimal. Additionally, there is no central repository for storing and retrieving related articles.

Results: In our approach we formulate a computational diagnostic ontology containing 91 elements, including classes and sub-classes, which are required to conduct Systematic Reviews-Meta Analysis (SR-MA) for diagnostic studies, which will assist in standardized reporting of diagnostic articles. SR-MA are studies that aggregate several studies to come to one conclusion for a particular research question. We also report high percentage of agreement among five observers as a result of the interobserver agreement test that we conducted among them to annotate 13 articles using the diagnostic ontology. Moreover, we extend our existing repository CERR-N to include diagnostic studies.

Availability: The ontology is available for download as an.owl file at: http://bioportal.bioontology.org/ontologies/3013.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Steps involved in designing the diagnostic ontology.
Figure 2
Figure 2. Hierarchy of classes present in the diagnostic ontology.
See this image and copyright information in PMC

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