We describe an approach to select semantically coherent specialty subsets based on the historical use of terminology by different service areas. Our approach uses rule-based and machine learning techniques to obtain a reduced set of 29 specialties.
Keywords: Specialties; classification; clustering; healthcare services; interoperability.