Rule extraction for process mining based on machine learning techniques

Abstract

Process mining is a discipline that has been gaining importance by offering a set of techniques that allow extracting knowledge from the event logs in which the information generated in the execution of processes is stored. One of the main objectives in process mining is to understand what has happened during the execution of a process. Typically, this goal is achieved by manually exploring the actual model, describing the behaviour of the process and temporal and frequency analytics on its variants and business indicators. In this paper, an innovative approach based on decision trees is presented that allows the automatic classification of certain behaviours that occur during a process based on the information generated during its executions and the variables associated with them, so that process stakeholders can have a better understanding of what is going on and thus improve decision making. This technique has been validated on a medical process, the Aortic Stenosis Integrated Care Process (AS ICP) implemented in the Cardiology Department of the University Hospital of Santiago de Compostela. On this process, the waiting times of patients have been tackled in order to extract those patient profiles susceptible to delays or to be prioritised.