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Knowledge-Based Trace Abstraction for Semantic Process Mining

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Artificial Intelligence in Medicine (AIME 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10259))

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Abstract

Many hospital information systems nowadays record data about the executed medical process instances in the form of traces in an event log. In this paper we present a framework able to convert actions found in the traces into higher level concepts, on the basis of domain knowledge. Abstracted traces are then provided as an input to semantic process mining. The approach has been tested in stroke care, where we show how the abstraction mechanism allows the user to mine process models that are easier to interpret, since unnecessary details are hidden, but key behaviors are clearly visible.

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References

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Author information

Authors and Affiliations

  1. DISIT, Computer Science Institute, Università del Piemonte Orientale, Alessandria, Italy

    Stefania Montani & Giorgio Leonardi

  2. Department of Computer Science, Università di Torino, Turin, Italy

    Manuel Striani

  3. Department of Electrical, Computer and Biomedical Engineering, Università di Pavia, Pavia, Italy

    Silvana Quaglini

  4. I.R.C.C.S. Fondazione “C. Mondino” - on Behalf of the Stroke Unit Network (SUN) Collaborating Centers, Pavia, Italy

    Anna Cavallini

Authors
  1. Stefania Montani
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  2. Manuel Striani
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  3. Silvana Quaglini
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  4. Anna Cavallini
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  5. Giorgio Leonardi
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Corresponding author

Correspondence to Stefania Montani .

Editor information

Editors and Affiliations

  1. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Annette ten Teije

  2. Medical University of Vienna, Vienna, Austria

    Christian Popow

  3. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    John H. Holmes

  4. University of Pavia, Pavia, Italy

    Lucia Sacchi

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Montani, S., Striani, M., Quaglini, S., Cavallini, A., Leonardi, G. (2017). Knowledge-Based Trace Abstraction for Semantic Process Mining. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-59758-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59757-7

  • Online ISBN: 978-3-319-59758-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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