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Supporting Computer-interpretable Guidelines’ Modeling by Automatically Classifying Clinical Actions

  • 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

Modeling computer-interpretable clinical practice guidelines is a complex and tedious task that has been of interest for several attempts to automate parts of this process. When modeling guidelines one of the tasks is to specify common actions in everyday’s practical medicine (e.g., drug prescription, observation) in order to link them with clinical information systems (e.g., an order-entry system). In this paper we compare a rule-based and a machine-learning method to classify activities according to the Clinical Actions Palette used in the Hybrid-Asbru ontology. We use syntactic and semantic features, such as the Semantic Types of the UMLS to classify the activities. Furthermore, we extend our methods by using 2-step classification and combining machine learning and rule-based approaches. Results show that machine learning performs better than the rule-based method on the classification task. They also show that the 2-step classification method improves the categorization of activities.

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Author information

Authors and Affiliations

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

    Anne-Lyse Minard & Katharina Kaiser

Authors
  1. Anne-Lyse Minard
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  2. Katharina Kaiser
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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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Minard, AL., Kaiser, K. (2013). Supporting Computer-interpretable Guidelines’ Modeling by Automatically Classifying Clinical Actions. 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_4

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  • DOI: https://doi.org/10.1007/978-3-319-03916-9_4

  • 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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