Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes

TL;DR

This paper introduces a semi-automatic process mining approach that optimizes model complexity and fitness, incorporating cycle collapsing and meta-states to improve interpretability, demonstrated on healthcare-related datasets.

Contribution

It presents a novel method combining model complexity assessment, simplification, and cycle collapsing through meta-states for enhanced process model interpretability.

Findings

Effective model simplification balances complexity and fitness.

Meta-states facilitate cycle collapsing and model interpretation.

Application to healthcare datasets demonstrates practical utility.

Abstract

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.