Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/42588
Title: From Script to Application. A bupaR Integration into PMApp for Interactive Process Mining Research
Authors: Tornero-Costa, Roberto
Fernandez-Llatas, Carlos
MARTIN, Niels 
JANSSENSWILLEN, Gert 
VAN HULZEN, Gerard 
Issue Date: 2024
Publisher: Springer
Source: Juarez, Jose M.; Fernandez-Llatas, Carlos; Bielza, Concha; Johnson, Owen; Kocbek, Primoz; Larrañaga, Pedro; Martin, Niels; Munoz-Gama, Jorge; Štiglic, Gregor; Sepulveda, Marcos; Vellido, Alfredo (Ed.). Explainable Artificial Intelligence and Process Mining Applications for Healthcare Third International Workshop, XAI-Healthcare 2023, and First International Workshop, PM4H 2023, Portoroz, Slovenia, June 15, 2023, Proceedings, Springer, p. 107 -117
Series/Report: Communications in Computer and Information Science
Series/Report no.: 2020
Status: Early view
Abstract: There are several open-source Process Mining tools available for research purposes. PMApp is an Interactive Process Mining toolkit developed to facilitate process discovery and analysis in healthcare. PMApp is designed to introduce healthcare professionals to Process Mining, simplifying the learning process by reducing the need for extensive coding knowledge. With PMApp, users can easily apply process mining techniques to healthcare data without the steep learning curve typically associated with coding languages. At the same time, bupaR is an open-source, integrated suite of R-packages for handling and analysing process data. bupaR provides support for different stages in process analysis and offers the flexibility of coding tools. Given the complementarity between both tools, this paper outlines how PMApp has been extended to integrate R code and bupaR functionalities. This integration provides an opportunity to extend research in process mining by combining two powerful tools. The paper will showcase the feasibility of this integration and demonstrate how researchers and clinicians can leverage the combined capabilities of Interactive Process Mining and scripting open-source tools to gain insights into complex healthcare processes.
Keywords: Interactive Process Mining in Healthcare;Interfaces for Process Oriented Data Science;Process Mining;R;Interactive Process Mining
Document URI: http://hdl.handle.net/1942/42588
ISBN: 9783031543029
9783031543036
DOI: 10.1007/978-3-031-54303-6_11
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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