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Process Mining and Conformance Checking of Long Running Processes in the Context of Melanoma Surveillance

Author

Listed:
  • Christoph Rinner

    (Center for Medical Statistics, Informatics, and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1010 Vienna, Austria
    Current address: Medical University Vienna, Spitalgasse 23, 1090 Vienna, Austria.)

  • Emmanuel Helm

    (Research Department of Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, Softwarepark 13, 4232 Hagenberg, Austria)

  • Reinhold Dunkl

    (Faculty of Computer Science, University of Vienna, Währinger Strasse 29, 1010 Vienna, Austria)

  • Harald Kittler

    (Department of Dermatology, Medical University of Vienna, Währinger Gürtel 18-20, 1010 Vienna, Austria)

  • Stefanie Rinderle-Ma

    (Faculty of Computer Science, University of Vienna, Währinger Strasse 29, 1010 Vienna, Austria)

Abstract

Background: Process mining is a relatively new discipline that helps to discover and analyze actual process executions based on log data. In this paper we apply conformance checking techniques to the process of surveillance of melanoma patients. This process consists of recurring events with time constraints between the events. Objectives: The goal of this work is to show how existing clinical data collected during melanoma surveillance can be prepared and pre-processed to be reused for process mining. Methods: We describe an approach based on time boxing to create process models from medical guidelines and the corresponding event logs from clinical data of patient visits. Results: Event logs were extracted for 1023 patients starting melanoma surveillance at the Department of Dermatology at the Medical University of Vienna between January 2010 and June 2017. Conformance checking techniques available in the ProM framework and explorative applied process mining techniques were applied. Conclusions: The presented time boxing enables the direct use of existing process mining frameworks like ProM to perform process-oriented analysis also with respect to time constraints between events.

Suggested Citation

  • Christoph Rinner & Emmanuel Helm & Reinhold Dunkl & Harald Kittler & Stefanie Rinderle-Ma, 2018. "Process Mining and Conformance Checking of Long Running Processes in the Context of Melanoma Surveillance," IJERPH, MDPI, vol. 15(12), pages 1-14, December.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:12:p:2809-:d:189457
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    Cited by:

    1. Emmanuel Helm & Anna M. Lin & David Baumgartner & Alvin C. Lin & Josef Küng, 2020. "Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare," IJERPH, MDPI, vol. 17(4), pages 1-12, February.
    2. Martin Kopecky & Hana Tomaskova, 2020. "The Business Process Model and Notation Used for the Representation of Alzheimer’s Disease Patients Care Process," Data, MDPI, vol. 5(1), pages 1-12, February.

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