Semantic models in biomedicine : building interoperating ontologies for biomedical data representation and processing in pharmacovigilance

• Main Author: Courtot, Melanie
• Language: English
• Published: University of British Columbia 2014
• Online Access: http://hdl.handle.net/2429/46804

It is increasingly challenging to analyze the data produced in biomedicine, even more so when relying on manual analysis methods. My hypothesis is that using a common representation of knowledge, implemented via standard tools, and logically formalized can make those datasets computationally amenable, help with data integration from multiple sources and allow to answer complex queries. The first part of this dissertation demonstrates that ontologies can be used as common knowledge models, and details several use cases where they have been applied to existing information in the domain of biomedical investigations, clinical data and vaccine representation. In a second part, I address current issues in developing and implementing ontologies, and proposes solutions to make ontologies and the datasets they are applied to available on the Semantic Web, increasing their visibility and reuse. The last part of my thesis then builds upon the first two, and applies their results to pharmacovigilance, and specifically to analysis of reports of adverse events following immunization. I encoded existing standard clinical guidelines from the Brighton Collaboration in Web Ontology Language (OWL) in the Adverse Events Reporting Ontology (AERO) I developed within the framework of the Open Biological and Biomedical Ontologies Foundry. I show that it is possible to automate the classification of adverse events using the AERO with very high specificity (97%). I also demonstrate that AERO can be used with other types of guidelines. Finally, my pipeline relies on open and widely used data standards (Resource Description Framework (RDF), OWL, SPARQL) for implementation, making the system easily transposable to other domains. This thesis validates the usefulness of ontologies as semantic models in biomedicine enabling automated, computational processing of large datasets. It also fulfills the goal of raising awareness of semantic technologies in the clinical community of users. Following my results the Brighton Collaboration is moving towards providing a logical representation of their guidelines.