Automated extraction of domain-specific clinical ontologies




A significant set of challenges in the use of large, source ontologies in the medical domain include: automated translation, customization of source ontologies, and performance issues associated with the use of logical reasoning systems to interpret the meaning of a domain captured in a formal knowledge representation. SNOMED-CT and FMA are two reference ontologies that cover much of the domain of clinical medicine and motivate a better means for the re-use of such ontologies. In this presentation, the author will present a set of automated methods (and tools) for segmenting, merging, and surveying modules extracted from these ontologies for a specific domain.


Chimezie Ogbuji is a senior research associate in the Clinical Investigations Department of the Case Western Reserve University School of Medicine where he is responsible for managing, developing, and implementing Clinical and Translational Science Collaborative (CTSC) projects as well as clinical, biomedical, and administrative informatics projects for the Case Comprehensive Cancer Center.

His research interests are in applied ontology, knowledge representation, content repository infrastructure, and medical informatics. He has a BS in computer engineering from the University of Illinois and is a part-time PhD student in the Case Western School of Engineering. He most recently appeared as a guest editor in IEEE Internet Computing's special issue on Personal Health Records in the August 2011 edition.


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