Ontologies in the Health Field

Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status of the Semantic Web. Although there is no consensus on a common definition of an ontology, it is necessary to understand their main features to be able to use them in a pertinent and efficient manner for data mining purposes. This chapter introduces the basic notions about ontologies, presents a survey of their use in medicine and explores some related issues: knowledge bases, terminology, and information retrieval. It also addresses the issues of ontology design, ontology representation, and the possible interaction between data mining and ontologies. INTRODUCTION Ontologies have become a privileged and almost unavoidable means to represent and exploit knowledge and data. This is true in many domains, and particularly in the health field. Health data and knowledge had been structured through medical classifications and taxonomies long before ontologies had acquired their pivot status of the semantic web. In the health field, there are still more than one hundred classifications (e.g., ICD10, MeSH, SNOMED), which makes it very difficult to exploit data coded according to one or the other, or several of these classifications. The UMLS (Unified Medical Language System) initiative tries to provide a unified access to these classifications, in the absence of an ontology of the whole medical domain still to come. DOI: 10.4018/978-1-60566-218-3.ch002