Physicians and healthcare organizations always collect large amounts of data during patient care. These
large and high-dimensional datasets are usually characterized by an inherent sparseness. Hence, the analysis
of these datasets to gure out interesting and hidden knowledge is a challenging task.
This paper proposes a new data mining framework based on generalized association rules to discover
multiple-level correlations among patient data. Specically, correlations among prescribed examinations,
drugs, and patient proles are discovered and analyzed at different abstraction levels. The rule extraction
process is driven by a taxonomy to generalize examinations and drugs into their corresponding categories.
To ease the manual inspection of the result, a worthwhile subset of rules, i.e., the non-redundant generalized
rules, is considered. Furthermore, rules are classied according to the involved data features (medical treatments
or patient proles) and then explored in a top-down fashion, i.e., from the small subset of high-level
rules a drill-down is performed to target more specic rules.
The experiments, performed on a real diabetic patient dataset, demonstrate the effectiveness of the proposed
approach in discovering interesting rule groups at different abstraction levels
