Arden syntax and GALEN terminology support: a powerful combination to represent medical knowledge.
Arden syntax is an ASTM standard defined a few years ago to represent rule-based knowledge in the medical domain in an application- and implementation-independent way. Knowledge is expressed as Medical Logic Modules (MLMs). So far, it has mainly been used for providing critique and reminders in data driven, medical decision support systems, where typically rules are evoked by certain database operations (e.g., "store systolic blood pressure"). The syntax is an important step towards a general medical knowledge representation, with version control, maintenance information, arithmetic operations, logic rule syntax, and temporal operations, but it is not yet complete. The standard does not define how medical concepts (e.g., "systolic blood pressure") are to be referenced. Existing coding schemes such as SNOMED and ICD10 may be used for this, but they have generally failed to provide the required level of detail. The use of Arden syntax to transfer knowledge between centers in the form of MLMs has been shown to be non-trivial [1,2]. GALEN (Generalized Architecture for Languages, Encyclopedias, and Nomenclatures in Medicine) is an EC AIM project aiming at a machine-readable system to represent medical concepts in a semantic network. The network allows for the combination and specialization of concepts to the required level of detail. The project has resulted in a notation (GRAIL: GALEN Representation and Integration Language) and several tools, such as browsers for manipulating medical concepts. In a GALEN subproject, we are exploring the potential utility of combining Arden syntax with semantics from the GALEN terminology, and we are developing tools to facilitate the creation of MLMs with terminology support. Our impression is that the combination is very useful: GALEN tools allow a domain expert to express her knowledge in purely medical terms, independent of an actual or intended patient database structure and data representation. They can also help a knowledge engineer or quite possibly an automatic interpreter to translate a well defined medical concept (expressed in GRAIL) to actual database queries (in SQL or other notation). This translation can be facilitated by a Data Dictionary describing the local patient database.
Thurin
A
A
Department of Medical Informatics, Linkoping University, Linkoping, Sweden.
Carlsson
M
M
Gill
H
H
Wigertz
O
O
D005103:Expert Systems; D009323:Natural Language Processing; D009626:Terminology as Topic