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Discovering Probabilistic Structures of Healthcare Processes

  • Conference paper
Process Support and Knowledge Representation in Health Care (ProHealth 2013, KR4HC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8268))

Abstract

Medical protocols and guidelines can be looked upon as concurrent programs, where the patient’s state dynamically changes over time. Methods based on verification and model-checking developed in the past have been shown to offer insight into the correctness of guidelines and protocols by adopting a logical point of view. However, there is uncertainty involved both in the management of the disease and the way the disease will develop, and, therefore, a probabilistic view on medical protocols seems more appropriate. Representations using Bayesian networks capture that uncertainty, but usually concern a single patient group and do not capture the dynamic nature of care. In this paper, we propose a new method inspired by automata learning to represent and identify patient groups for obtaining insight into the care that patients have received. We evaluate this approach using data obtained from general practitioners and identify significant differences in patients who were diagnosed with a transient ischemic attack. Finally, we discuss the implications of such a computational method for the analysis of medical protocols and guidelines.

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Author information

Authors and Affiliations

  1. Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands

    Arjen Hommersom, Sicco Verwer & Peter J. F. Lucas

Authors
  1. Arjen Hommersom
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  2. Sicco Verwer
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  3. Peter J. F. Lucas
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Editor information

Editors and Affiliations

  1. Dept. d’Enginyeria Informàtica i Matimàtiques, Research Group on Artificial Intelligence, Universitat Rovira i Virgili, Av. Països Catalans 26, 43007, Tarragona, Spain

    David Riaño

  2. Department of Computer Science, University of Erlangen-Nuremberg, Martensstraße 3, 91058, Erlangen, Germany

    Richard Lenz

  3. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstrasse 9-11/188, 1040, Vienna, Austria

    Silvia Miksch

  4. Department of Information Systems, University of Haifa, Rabin Bldg., 31905, Haifa, Israel

    Mor Peleg

  5. Institute of Databases and Information Systems, Ulm University, James-Franck-Ring, 89069, Ulm, Germany

    Manfred Reichert

  6. Department of AI, Free University Amsterdam, De Boelelaan 1081A, 1081 HV, Amsterdam, The Netherlands

    Annette ten Teije

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Hommersom, A., Verwer, S., Lucas, P.J.F. (2013). Discovering Probabilistic Structures of Healthcare Processes. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., ten Teije, A. (eds) Process Support and Knowledge Representation in Health Care. ProHealth KR4HC 2013 2013. Lecture Notes in Computer Science(), vol 8268. Springer, Cham. https://doi.org/10.1007/978-3-319-03916-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-03916-9_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03915-2

  • Online ISBN: 978-3-319-03916-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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