Relationships Between Change Patterns in Dynamic Event Attributes

Process mining utilizes process execution data to discover and analyse business processes. Event logs represent process execution data, providing information about activities executed in a process instance. In addition to generic event attributes like activity and timestamp, events might contain domain-specific attributes, such as a blood sugar measurement in a healthcare environment. Many of these values change during a typical process quite frequently. Hence, we refer to those as dynamic event attributes. Change patterns can be derived from dynamic event attributes, describing if the attribute values change from one activity to another. However, change patterns can only be identified in an isolated manner, neglecting the chance of finding co-occuring change patterns. This paper provides an approach to identify relationships between change patterns. We applied the proposed technique on the MIMIC-IV real-world dataset on hospitalizations in the US and evaluated the results with a medical expert. The approach is implemented in Python using the PM4Py framework.