Structured analysis of knowledge
International Journal of Man-Machine Studies
Applied Artificial Intelligence
Systematic knowledge base design for medical diagnosis
Applied Artificial Intelligence
Integrating classification-based compiled level reasoning with function-based deep level reasoning
Applied Artificial Intelligence
Mesicar-a medical expert system integrating causal and associative reasoning
Applied Artificial Intelligence
Using causal reasoning in gait analysis
Applied Artificial Intelligence
A munin network for the median nerve-a case study on loops
Applied Artificial Intelligence
ESDAT - An Expert System for Primary Medical Care
GWAI '83 Proceedings of the 7th German Workshop on Artificial Intelligence
Mdx2: an integrated medical diagnostic system
Mdx2: an integrated medical diagnostic system
Causal understanding of patient illness in medical diagnosis
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
MUNIN: a causal probabilistic network for interpretation of electromyographic findings
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Artificial Intelligence in Medicine
European research efforts in medical knowledge-based systems
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
AI in medicine on its way from knowledge-intensive to data-intensive systems
Artificial Intelligence in Medicine
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Diagnostic decisions in rheumatology are based to a large extent on a good understanding of the anatomy of the human body. A decision support system for rheumatology has to represent this fundamental anatomical knowledge in order to be able to reason about causal relationships between disturbances affecting the musculoskeletal system. We have built a knowledge-based system incorporating a detailed representation of the anatomy. This yields two main advantages: (1) it enables us to build generic disease descriptions. Instantiation automatically constructs specific disease descriptions by filling in the anatomical details which describe the situation of the patient; (2) the system provides a user interface showing all the anatomical details within the context of the patient's problem. This is essential for the intended field of application, namely, primary medical care. This paper concentrates on the usage of the anatomical knowledge during hypothesis formation and during hypothesis testing.