Incremental Development of Diagnostic Set-Covering Models with Therapy Effects

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.