Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts

Sina NAMAKI ARAGHI
* ORCID logo ,
Franck FONTANILI
,
Arkopaul SARKAR
,
Elyes LAMINE
,
Mohammed-Hedi KARRAY
ORCID logo ,
Frederick BENABEN
Version 1 : Received: 21 December 2023 / Approved: 24 December 2023 / Online: 25 December 2023 (08:22:48 CET)

A peer-reviewed article of this Preprint also exists.

Namaki Araghi, S.; Fontanili, F.; Sarkar, A.; Lamine, E.; Karray, M.-H.; Benaben, F. DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts. Modelling 2024, 5, 85-98. Namaki Araghi, S.; Fontanili, F.; Sarkar, A.; Lamine, E.; Karray, M.-H.; Benaben, F. DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts. Modelling 2024, 5, 85-98.

Abstract

Process mining offers significant services in the development of digital threads and digital twin applications for healthcare. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes is an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. To address this, we present the DIAG approach that consists of a meta-model and an algorithm that enables diagnosing in process mining through an ontology-driven approach. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of the DIAG approach.

Keywords

process mining; ontology; care pathways; digital thread; model-based system engineering; real-time location systems

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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