Technical Reports - Query Results

ÖFAI-TR-92-16 ( 423kB g-zipped PostScript file)

Automatic Knowledge Base Refinement: Learning from Examples and Deep Knowledge in Rheumatology

Gerhard Widmer, Werner Horn, Bernhard Nagele

MESICAR is a second generation expert system which contains very general disease descriptions about rheumatological disorders in the primary medical care field. With the help of a detailed hierarchical description of the human anatomy the system is able to support diagnostic decisions. The current paper describes how machine learning techniques are used to automatically build more specific disease descriptions for common, frequently occurring cases. The system MESICAR-LEARN implements a learning method which integrates analytical and empirical learning techniques. Cases diagnosed by MESICAR form the training examples, and MESICAR's knowledge base is used as domain theory. The learned concepts are integrated into a hierarchy of disease descriptions. They support efficient and fast reasoning on common cases in addition to the general diagnostic support afforded by MESICAR's deep knowledge.

Keywords: , Medical Expert System, Rheumatology, Deep Knowledge, Machine Learning, Knowledge Base Refinement

Citation: Widmer G., Horn W., Nagele B.: Automatic Knowledge Base Refinement: Learning from Examples and Deep Knowledge in Rheumatology, Artificial Intelligence in Medicine 5 (3), pp. 225-243, special issue on ``Expert Systems, Knowledge Acquisition, and Learning'', 1993.