Summary
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.
Inthis paperwepresent 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.
Keywords
Temporal data mining - temporal association rules - hybrid events - administrative
healthcare data