Terminology support for development of sharable knowledge modules

Abstract

Lack of an agreed infrastructure for terminology is identified as one of the major barriers to interchange of knowledge modules and integration of knowledge bases with other clinical information systems. The goal of the GALEN project is to bridge this gap between different terminology systems through the construction of a terminology server, which is based on a rich conceptual model with mapping facilities to natural language expressions and coding schemas. The long term goal is to support communication between medical information systems. Arden Syntax is a standard format for the creation of knowledge modules, with sharability as one of the main objectives. Since Arden Syntax is based on a data-driven approach, the data items used need to be adapted to locally available terminology. The GALEN approach appears to be complementary to Arden Syntax and to the development of sharable knowledge modules. The major theme of this paper is utilization of the GALEN terminology server for knowledge module authoring. Two systems are presented, a knowledge base manager and a client to the terminology server, allowing the user to navigate in the semantic network and to import concept definitions and terms into the knowledge modules. The benefit of the terminology server, allowing the user to navigate in the semantic network and to import concept definitions and terms into the knowledge modules. The benefit of the terminology services is discussed.

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

“…However, effective utilization of medical terminology systems as a knowledge-base and for knowledge representation and storage requires the development of mapping mechanisms between different terminology systems and between terminology systems and natural language expressions and electronic database schemas. Toward this end, various researchers have focused on embedding clinical knowledge into healthcare information systems by developing mappings between specialized glossaries such as SNOMED and DICOM [Bidgood, 1998], mapping natural language expressions to coding schemes [Carlsson et al, 1996;Geissbuhler and Miller, 1998], integration of terminologies [Choi et al, 2005;Chute et al, 1998;Elkin and Brown, 2002;Park et al, 2007] or by embedding medical data dictionaries into healthcare information systems [Burkle et al, 1998]. Other studies on medical terminology systems include the evaluation of completeness of medical concept vocabularies for representing medical procedures [Bodenreider et al, 1998;Dykes et al, 2003] and validating their structures [Cornet and Abu-Hanna, 2005;Rogers et al, 1998].…”