The usefulness of ontologies in health research: focus on epidemiology Claudia Pagliari PhD FRCPE eHealth Research Group The University of Edinburgh Ontology Network meeting, Edinburgh April 2013

In which areas of medicine are ontologies used ? Management & exchange of clinical data (drugs, symptoms, history, patient characteristics etc..) Medical knowledge collation for evidence- based practice Data reuse for epidemiological research Information-intensive biomedical research e.g. Bowman (2005). "Coordinating SNOMED-CT and ICD-10: Getting the Most out of Electronic Health Record Systems." Journal of AHIMA 76, no.7, Chan, J. and Kishore, R. and Sternberg, P. and Van Auken, K. (2012) The Gene Ontology: enhancements for Nucleic Acids Research, 40 (D1). D559-D564. ISSN [Cradle-to-grave Patient Data in NHS Scotland]

3 phases of ontologies in eHealth 1) Focus on terminology & coding schemes. Dominated by library science. Oriented towards cataloguing & indexing of published literature – e.g. MESH 2) Focus on database design & software. Dominated by programmers. Oriented towards describing & promoting access to data – e.g. HL7 Reference Information Model 3) Focus on biological and patient reality. Biologists more involved in ontology development – e.g. Gene Ontology (GO); Open Biomedical Ontologies (OBO) Smith & Brochhausen (2012), “Establishing and Harmonizing Ontologies in an Interdisciplinary Health Care and Clinical Research Environment”, in: B. Blobel P. Pharow and M. Nerlich (eds.), eHealth: Combining Health Telematics, Telemedicine, Biomedical Engineering and Bioinformatics on the Edge (Global Expert Summit Textbook, Studies in Health, Technology and Informatics, 134), IOS Press, Amsterdam,

Data linkage in epidemiology & the need for ontologies

Data linkage initiatives spawning…

– Epidemiological studies (frequencies, trends, associations) – Natural experiments – (evaluation of new interventions or policies e.g. smoking ban) – Pharmacosurveillance (e.g. WoSCoPS) etc. Population-based observational research

Ontologies & semantics in epidemiology Epidemiology research is multidisciplinary Requires integrative frameworks for data sharing Semantic Web may help to: – improve interoperability – enable machines to trace & process resources – converge epidemiological & predictive models – find hidden patterns in epidemiological data Requires effective use of ontologies (rate-limiting) Need to evaluate ontologies against outcomes & prioritise quality Ferreira (2013) On the usefulness of ontologies in epidemiological research. J Epidemiol Community Health, Vol 67 No 5 Hohendorf (2012) Evaluation of research in biomedical ontologies. Briefings in Bioinformatics Advance Access, Sept 8