Profiling Examiners using Intelligent Subgroup Mining

The demand for effective knowledge discovery methods in a clinical setting is growing: the number of hospital information systems and medical documentation systems in routine-use increases rapidly. Then, often high-quality collections of electronic patient records are available for statistical analysis. One interesting issue concerns the quality of the examinations records which depends both on the examination quality and the documentation habits of the individual examiners. We apply a subgroup mining approach for explorative and descriptive data mining to tackle this issue, and we provide a case study of the proposed approach using data from a fielded system in the medical domain. Purely automatic data mining methods often suffer from the limitation that too many uninteresting results are presented to the user. In order to improve upon this situation, we propose two strategies: we use background knowledge, if available, and provide suitable visualizations for guiding the discovery process. The context of the presented approach is a knowledge-based documentation and consultation system.