Selecting and Reporting What is Interesting The KEFIR Application to Healthcare Data

One of the most promising areas in Knowledge Discovery in Databases is the automatic analysis of deviations Success in this task hinges on the ability to identify a few important and relevant events among the multitude of potentially interesting deviations In this chapter we present our approach to determining the interestingness of a deviation via the potential bene t from a relevant action This approach has been implemented in the Key Findings Reporter KEFIR a system for discovering and explaining key ndings in large changing databases currently being applied to the analysis of healthcare data The system performs an automatic drill down through data along multiple dimensions to determine the most interesting deviations of speci c quantitative measures relative to their previous and expected values It explains key deviations through their relationship to other deviations in the data and where appropriate generates recommendations for actions in response to these deviations KEFIR uses Mosaic a WWW browser to present its ndings in a hypertext report using natural language and business graphics Matheus Piatetsky Shapiro and McNeill