Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

Araghi, Sina Namaki; Fontanili, Franck; Lamine, Elyes; Salatgé, Nicolas; Bénaben, Frédérick

doi:10.5220/0008971104720481

Cited by 6 publications

(4 citation statements)

References 31 publications

(40 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We focus on analyzing patients in the urology department. To better understand how the data was inter-preted and prepared for process discovery actions, we refer to the works in [32,33]. • Sepsis Data set [34]: This event log consists of sepsis cases in a hospital.…”

Section: Case Studies: Discussion and Evaluationmentioning

confidence: 99%

Stable Heuristic Miner 2: Evaluating the Statistical Stability in Event Logs to Discover Business Processes

Namaki Araghi,

Fontanili,

Lamine

et al. 2024

Hum-Cent Intell Syst

Self Cite

View full text Add to dashboard Cite

Automated process discovery as one of the paradigms of process mining has attracted both industries and academic researchers. These methods offer visibility and comprehension out of complex and unstructured event logs. Over the past decade, the classic heuristic miner and applied heuristic-based process discovery algorithms showed promising results in revealing the hidden process patterns in information systems. One of the challenges related to such algorithms is the arbitrary selection of recorded behaviors in an event log. The offered filtering thresholds are manually adjustable, which could lead to the extraction of a non-optimal process model. This is also visible in commercial process mining solutions. Recently, the first version of the stable heuristic miner algorithm targeted this issue by evaluating the statistical stability of an event log. However, the previous version was limited to evaluating only activities’ behaviors. In this article, we’ll be evaluating the statistical stability of both activities and edges of a graph, which could be discovered from an event log. As a contribution, the stable heuristic miner 2 is introduced. Consequently, the definition of the descriptive reference process model has improved. The novel algorithm is evaluated by using two real-world event logs. These event logs are the familiar Sepsis data set and the urology department patients’ pathways event log, which is recorded by monitoring the interpreted location data of patients on hospital premises and is shared with the scientific community in this article.

show abstract

Section: Case Studies: Discussion and Evaluationmentioning

confidence: 99%

Stable Heuristic Miner 2: Evaluating the Statistical Stability in Event Logs to Discover Business Processes

Namaki Araghi,

Fontanili,

Lamine

et al. 2024

Hum-Cent Intell Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…Locations are determined by means of wireless technologies as radio frequency identification (RFID) tags, integrated in patient identification bands or staff cards, and antennas which are deployed in the hospital's departments. RTLS data have been used for PM to collect patients' movements (Araghi et al, 2019(Araghi et al, , 2020Araghi, Fontaili, et al, 2018;Araghi, Fontanili, et al, 2018;Fern andez-Llatas et al, 2013;Martinez-Millana et al, 2019), to mine the workflow of medical equipments (Liu et al, 2014), to study a surgical process (Fernandez-Llatas et al, 2015), and to enhance event logs extracted from a HIS (Fernandez-Llatas et al, 2021;Martin, 2018).…”

Section: Healthcare Datamentioning

confidence: 99%

“…Automated (Abo-Hamad, 2017; Alharbi et al, 2018;Alvarez et al, 2018;Amantea et al, 2020;Antonelli & Bruno, 2015;Araghi et al, 2020;Araghi, Fontaili, et al, 2018;Baker et al, 2017;Bouarfa & Dankelman, 2012;Caron et al, 2011;Caron et al, 2014;B. Chen et al, 2021;Chiudinelli et al, 2020;Cho et al, 2014;Dagliati et al, 2014;De Oliveira et al, 2020;De Weerdt et al, 2012;Duma & Aringhieri, 2020;Fei et al, 2010;Fernandez-Llatas et al, 2015;Fern andez-Llatas et al, 2010;Fern andez-Llatas, Garcia-Gomez, et al, 2011;Furniss et al, 2016;Garg & Agarwal, 2016;A.…”

Section: Discovery Approach Referencesunclassified

Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Process mining (PM) is a well-known research area that includes techniques, methodologies, and tools for analyzing processes in a variety of application domains. In the case of healthcare, processes are characterized by high variability in terms of activities, duration, and involved resources (e.g., physicians, nurses, administrators, machineries, etc.). Besides, the multitude of diseases that the patients housed in healthcare facilities suffer from makes medical contexts highly heterogeneous. As a result, understanding and analyzing healthcare processes are certainly not trivial tasks, and administrators and doctors look for tools and methods that can concretely support them in improving the healthcare services they are involved in. In this context, PM has been increasingly used for a wide range of applications as reported in some recent reviews. However, these reviews mainly focus on discussion on applications related to the clinical pathways, while a systematic review of all possible applications is absent. In this article, we selected 172 papers published in the last 10 years, that present applications of PM in the healthcare domain. The objective of this study is to help and guide researchers interested in the medical field to understand the main PM applications in the healthcare, but also to suggest new ways to develop promising and not yet fully investigated applications. Moreover, our study could be of interest for practitioners who are considering applications of PM, who can identify and choose PM algorithms, techniques, tools, methodologies, and approaches, toward what have been the experiences of success.

show abstract

“…Looking at C2 and C3 in Table 1, it is important for process mining applications to provide extended information about healthcare processes, such as causes of drifts and deviations in processes. Research has shown that diagnosing inefficiencies and reasoning in healthcare processes are often ignored [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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

Namaki Araghi,

Fontanili,

Sarkar

et al. 2023

Modelling

View full text Add to dashboard Cite

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.

show abstract

Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

Cited by 6 publications

References 31 publications

Stable Heuristic Miner 2: Evaluating the Statistical Stability in Event Logs to Discover Business Processes

Stable Heuristic Miner 2: Evaluating the Statistical Stability in Event Logs to Discover Business Processes

Process mining applications in the healthcare domain: A comprehensive review

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

Contact Info

Product

Resources

About