Building a relational database for a physician document index

We show how three existing medical knowledge bases: Medical Subject Headings (MeSH), Standardized Nomenclature of Medicine (SNOMED) and Current Medical Information and Technology (CMIT) are mapped into a relational data model and stored on an Apollo workstation and an Intelligent Database Machine. Since two of these knowledge bases have been used in the indexing of medical literature and patient records, they can be useful not only as direct views on the organization of medical concepts but also as tools for the retrieval of documents. In order that the concepts from one knowledge base can be connected to those of the other knowledge base, a method has been developed for the semi-automatic merging of MeSH, SNOMED and CMIT. This method takes advantage of the relational model and the synonyms that are given in SNOMED and CMIT, in order to recommend concepts to be merged. An expert interacts with the system to accept or reject the recommendations of the computer. The method would apply equally well to other knowledge bases and is particularly well-suited for knowledge bases that contain tens of thousands of concepts.