Personalized medicine benefits from the identification of subpopulations that exhibit higher prevalence of a disease than the general population: such subpopulations can become the target of more intensive investigations to identify risk factors and to develop dedicated therapies. Classification rule discovery algorithms are an appropriate tool for discovering such subpopulations: they scale well, even for multi-dimensional data, are not affected by missing values, and deliver comprehensible patterns. However, such algorithms may generate hundreds of rules and thus call for exploration methods. In this study, we extend the tool Interactive Medical Miner for the discovery of classification rules, into the Interactive Classification rule Explorer ICE, which offers functionalities for drill-in rule exploration, grouping, rule visualization and statistics. We report on our first results for the classification of cohort data on goiter, a disorder of the thyroid gland.