Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes
This work addresses the need for more interpretable and balanced process models in healthcare, but it is incremental as it builds on existing process mining techniques.
The paper tackles the problem of balancing model complexity and fitting accuracy in process mining by proposing a semi-automatic optimization approach that includes model simplification and cyclic states identification, applied to healthcare datasets such as remote monitoring for hypertension and COVID-19 workflows, resulting in improved interpretability and balance without specific numerical gains reported.
Within Process mining, discovery techniques had made it possible to construct business process models automatically from event logs. However, results often do not achieve the balance between model complexity and its fitting accuracy, so there is a need for manual model adjusting. The paper presents an approach to process mining providing semi-automatic support to model optimization based on the combined assessment of the model complexity and fitness. To balance between the two ingredients, a model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity. Additionally, we introduce a concept of meta-states, a cycle collapsing in the model, which can potentially simplify the model and interpret it. We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain. They are remote monitoring process for patients with arterial hypertension and workflows of healthcare workers during the COVID-19 pandemic. A case study also investigates the use of various complexity measures and different ways of solution application providing insights on better practices in improving interpretability and complexity/fitness balance in process models.