Temporal data abstraction bridges the gap between snap shot values delivered by monitoring devices and laboratory tests on one side and high-level medical concepts used in guidelines and by medical professionals on the other side. Within this field, the detection and abstraction of repeated patterns is a complex and important challenge. A repeated pattern is a combination of events or intervals which occur multiple times in a formally describable temporal relation.

While there are many approaches to detect patterns in time series without prior definition of target concepts, we describe the application of temporal data abstraction in the context of guideline execution. Here predefined concepts of temporal patterns must be compared with measurement series describing the patient state. We discuss the requirements coming from both high-frequency domains such as intensive care units and low-frequency domains such as diabetes monitoring and show our solution based on a new version of the Asgaard data abstraction unit. It interfaces the dynamically changing patient state to the guideline execution unit and features abstraction modules ranging from simple calculations to statistical measures calculated for sliding time windows.