利用模型复杂度优化和循环状态识别扩展过程发现：在医疗保健过程中的应用
Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes
摘要：
在流程挖掘中，发现技术使得从事件日志自动构建业务流程模型成为可能。然而，结果往往不能达到模型复杂性和拟合精度之间的平衡，因此需要手动调整模型。提出了一种基于模型复杂度和适应度综合评估的过程挖掘方法，为模型优化提供半自动支持。为了平衡这两个因素，提出了一种模型简化方法，它本质上是在所需的粒度抽象的原始模型。此外，我们还引入了元状态的概念，即模型中的循环崩溃，这可能会简化模型并解释它。我们的目标是使用来自医疗保健领域不同应用程序的三个数据集来展示技术解决方案的功能。它们是2019冠状病毒病大流行期间动脉高血压患者的远程监测过程和医护人员的工作流程。一个案例研究还调查了使用各种复杂性措施和不同的解决方案的应用提供更好的做法，在提高可解释性和复杂性/健身平衡过程模型的见解。
英文摘要：
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.
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