Automatic knowledge base refinement: Learning from examples and deep knowledge in rheumatology

被引：9

作者：

Widmer, G. ^{[1
]}

Horn, W. ^{[1
]}

Nagele, B. ^{[1
]}

机构：

[1] Department of Medical Cybernetics, University of Vienna, Freyung 6, 1010 Vienna, Austria

来源：

Artificial Intelligence in Medicine | 1993年 / 5卷 / 03期

关键词：

Computer aided diagnosis - Decision support systems - Expert systems - Knowledge based systems;

D O I：

10.1016/0933-3657(93)90026-Y

中图分类号：

学科分类号：

摘要：

MESICAR is a second generation expert system which contains very general descriptions of 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 paper describes how machine learning techniques are used to automatically construct 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.

引用

页码：225 / 243