Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Medical diagnosis using a probabilistic causal network
Applied Artificial Intelligence
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Knowledge reuse among diagnostic problem-solving methods in the shell-kit D3
International Journal of Human-Computer Studies
Data mining: concepts and techniques
Data mining: concepts and techniques
Using Compiled Knowledge to Guide and Focus Abductive Diagnosis
IEEE Transactions on Knowledge and Data Engineering
Inductive Learning for Case-Based Diagnosis with Multiple Faults
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
HepatoConsult: a knowledge-based second opinion and documentation system
Artificial Intelligence in Medicine
KnowWE: a Semantic Wiki for knowledge engineering
Applied Intelligence
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Although a lot of work in the field of knowledge acquisition has been done, the manual development of diagnostic knowledge systems by domain experts still is a very complex task. In this paper we will present an incremental approach for building diagnostic systems based on set-covering models. We start with a simple model describing the coarse structure between diagnoses and findings. Subsequently, this simple model can be enhanced by similarities, weights and probabilities to increase the accuracy of the knowledge and the resulting system. We will also show how these static set-covering models can be combined with dynamic set-covering models including higher level knowledge about causation effects. We will motivate how dynamic set-covering models can be used for implementing diagnostic systems including therapy effects. Finally, we report on two practical applications dealing with set-covering models from the geoecological and from the medical domain, respectively, that we have implemented.