Mining Health Care Administrative Data with Temporal Association Rules on Hybrid Events

Objective: The analysis of administrative health care data can be helpful to conveniently assess health care activities. In this context temporal data mining techniques can be suitably exploited to get a deeper insight into the processes underlying health care delivery. In this paper we present an algorithm for the extraction of temporal association rules (TARs) on sequences of hybrid events and its application on health care administrative databases. Methods: We propose a method that extends TAR mining by managing hybrid events, namely events characterized by a heterogeneous temporal nature. Hybrid events include both point-like events (e.g. ambulatory visits) and interval-like events (e.g. drug consumption). The definition of user-defined rule templates can be optionally used to constrain the search only to the extraction of a subset of interesting rules. A TAR post-pruning strategy, based on a case-control approach, is also presented. Results: We analyzed the administrative database of diabetic patients in charge to the regional health care agency (ASL) of Pavia. TAR mining allowed to find patterns specifically related to the diabetic population in comparison with a control group, as well as to check the compliance of the actual clinical careflow with the ASL recommendations. Conclusion: The experimental results highlighted the main potentials of the algorithm, such as the opportunity to detect interesting temporal relationships between diagnostic or therapeutic patterns, or to check the adherence of past temporal behaviors to specific expected paths (e.g. guidelines) or to discover new knowledge that could be implicitly hidden in the data.