DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts
The remarkable growth of process mining applications in care pathway monitoring is undeniable. 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, ther...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Modelling |
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| Online Access: | https://www.mdpi.com/2673-3951/5/1/6 |
| _version_ | 1797239964266659840 |
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| author | Sina Namaki Araghi Franck Fontanili Arkopaul Sarkar Elyes Lamine Mohamed-Hedi Karray Frederick Benaben |
| author_facet | Sina Namaki Araghi Franck Fontanili Arkopaul Sarkar Elyes Lamine Mohamed-Hedi Karray Frederick Benaben |
| author_sort | Sina Namaki Araghi |
| collection | DOAJ |
| description | The remarkable growth of process mining applications in care pathway monitoring is undeniable. 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 constitute an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. Therefore, we mitigate the issue by introducing cognitive process mining through the DIAG approach, which consists of a meta-model and an algorithm. This approach enables reasoning and diagnosing in process mining through an ontology-driven framework. 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 our approach. |
| first_indexed | 2024-04-24T17:59:54Z |
| format | Article |
| id | doaj.art-e7ed53cc577c47ac8b8d32513f574d3c |
| institution | Directory Open Access Journal |
| issn | 2673-3951 |
| language | English |
| last_indexed | 2024-04-24T17:59:54Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Modelling |
| spelling | doaj.art-e7ed53cc577c47ac8b8d32513f574d3c2024-03-27T13:56:29ZengMDPI AGModelling2673-39512023-12-0151859810.3390/modelling5010006DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept DriftsSina Namaki Araghi0Franck Fontanili1Arkopaul Sarkar2Elyes Lamine3Mohamed-Hedi Karray4Frederick Benaben5Production Engineering Laboratory (LGP), National Engineering School of Tarbes (ENIT), Tarbes University of Technology (UTTOP), 65000 Tarbes, FranceIndustrial Engineering Center (CGI) of IMT Mines Albi, 81000 Albi, FranceProduction Engineering Laboratory (LGP), National Engineering School of Tarbes (ENIT), Tarbes University of Technology (UTTOP), 65000 Tarbes, FranceIndustrial Engineering Center (CGI) of IMT Mines Albi, 81000 Albi, FranceProduction Engineering Laboratory (LGP), National Engineering School of Tarbes (ENIT), Tarbes University of Technology (UTTOP), 65000 Tarbes, FranceIndustrial Engineering Center (CGI) of IMT Mines Albi, 81000 Albi, FranceThe remarkable growth of process mining applications in care pathway monitoring is undeniable. 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 constitute an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. Therefore, we mitigate the issue by introducing cognitive process mining through the DIAG approach, which consists of a meta-model and an algorithm. This approach enables reasoning and diagnosing in process mining through an ontology-driven framework. 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 our approach.https://www.mdpi.com/2673-3951/5/1/6process miningontologycognitive process miningmodel-based system engineeringhealthcarereal-time location systems |
| spellingShingle | Sina Namaki Araghi Franck Fontanili Arkopaul Sarkar Elyes Lamine Mohamed-Hedi Karray Frederick Benaben DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts Modelling process mining ontology cognitive process mining model-based system engineering healthcare real-time location systems |
| title | DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts |
| title_full | DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts |
| title_fullStr | DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts |
| title_full_unstemmed | DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts |
| title_short | DIAG Approach: Introducing the Cognitive Process Mining by an Ontology-Driven Approach to Diagnose and Explain Concept Drifts |
| title_sort | diag approach introducing the cognitive process mining by an ontology driven approach to diagnose and explain concept drifts |
| topic | process mining ontology cognitive process mining model-based system engineering healthcare real-time location systems |
| url | https://www.mdpi.com/2673-3951/5/1/6 |
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