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Knowledge translation of SAGE-based guidelines for executing with knowledge engine
Jeong Ah Kim1, InSook Cho, Yoon Kim
1Computer Education Department of Kwandong University, Korea.
Abstract:
SAGE is very powerful knowledge representation for guideline modeling and well-defined knowledge framework to integrate with terminology standard and EMR databases. Therefore, SAGE can be powerful tool for knowledge authoring for clinicians but guideline execution engine is not available yet. Commercial rule engines are verified for availability in clinical area but their authoring tools are not matured for clinical knowledge. In this paper, we suggest knowledge translator to convert SAGE-based guidelines into knowledge which commercial engine can execute. With this translation, we can take both advantages in modeling power of SAGE and interpretation capability of engines.
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