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

arXiv: https://arxiv.org/abs/2206.06111 · HTML: https://mlsanity.com/paper/2206.06111/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 69 references that are in the map.
- First posted on arXiv 10 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Automated Discovery of Business Process Simulation Models from Event Logs](https://mlsanity.com/paper/1910.05404/) (2019)
- [Event Abstraction for Process Mining using Supervised Learning Techniques](https://mlsanity.com/paper/1606.07283/) (2016)
- [Simulation of Patient Flow in Multiple Healthcare Units using Process and Data Mining Techniques for Model Identification](https://mlsanity.com/paper/1702.07733/) (2017)

## Abstract (arXiv)

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.