Detecting deviations between event log and process model

A method for updating a process model for treatment of diabetes, the method comprising:

• detecting deviations between an event log and the process model by:
• converting, with a processing device, the process model into a probability process model for treatment of diabetes, the probability process model comprising multiple nodes in multiple hierarchies and probability distribution associated with the multiple nodes, a leaf node among the multiple nodes corresponding to a diabetes treatment activity in the process model for treatment of diabetes, the diabetes treatment activity being selected from the group consisting of an HbAlc test, a blood sugar test, and a serum creatinine test, a non-leaf node among the multiple nodes corresponding to a phase in the process model, wherein the converting the process model into a probability process model comprises calculating start probabilities of occurrence of the non-leaf nodes, transition probabilities among the non-leaf nodes and emission probabilities from the non-leaf nodes to the leaf nodes, so as to form the probability process model;
• detecting differences between at least one event sequence contained in the event log and the probability process model according to a correspondence relationship, wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and the leaf node in the probability process model, and further wherein the types of the differences comprise violated constraints and determining the differences comprises:
• using linear temporal logic to construct an automaton conforming to constraints in the probability process model;
• and
• obtaining the violated constraints from a path sequence in the at least one event sequence that cannot be accepted by the automaton;
• and
• identifying the differences as the deviations in response to the differences exceeding a predefined threshold;
• wherein the correspondence relationship describes a correspondence relationship between an event in one event sequence of the at least one event sequence and a leaf node in the probability process model;
• updating the process model based on the deviations and
• adding new diabetes treatment activities based on the deviations to the process model.