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A Semi-automated Method for Domain-Specific Ontology Creation from Medical Guidelines

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Enterprise, Business-Process and Information Systems Modeling (BPMDS 2022, EMMSAD 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 450))

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Abstract

The automated capturing and summarization of medical consultations has the potential to reduce the administrative burden in healthcare. Consultations are structured conversations that broadly follow a guideline with a systematic examination of predefined observations and symptoms to diagnose and treat well-defined medical conditions. A key component in automated conversation summarization is the matching of the knowledge graph of the consultation transcript with a medical domain ontology for the interpretation of the consultation conversation. Existing general medical ontologies such as SNOMED CT provide a taxonomic view on the terminology, but they do not capture the essence of the guidelines that define consultations. As part of our research on medical conversation summarization, this paper puts forward a semi-automated method for generating an ontological representation of a medical guideline. The method, which takes as input the well-known SNOMED CT nomenclature and a medical guideline, maps the guidelines to a so-called Medical Guideline Ontology (MGO), a machine-processable version of the guideline that can be used for interpreting the conversation during a consultation. We illustrate our approach by discussing the creation of an MGO of the medical condition of ear canal inflammation (Otitis Externa) given the corresponding guideline from a Dutch medical authority.

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Authors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands

    Omar ElAssy, Rik de Vendt, Fabiano Dalpiaz & Sjaak Brinkkemper

Authors
  1. Omar ElAssy
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  2. Rik de Vendt
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  3. Fabiano Dalpiaz
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  4. Sjaak Brinkkemper
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Corresponding author

Correspondence to Sjaak Brinkkemper .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Carlton, VIC, Australia

    Adriano Augusto

  2. University of Technology Sydney, Ultimo, NSW, Australia

    Asif Gill

  3. TU Wien, Vienna, Austria

    Dominik Bork

  4. University Paris 1 Pantheon-Sorbonne, Paris, France

    Selmin Nurcan

  5. University of Haifa, Haifa, Israel

    Iris Reinhartz-Berger

  6. Hochschule für angewandte Wissenschaften München, Munich, Germany

    Rainer Schmidt

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ElAssy, O., de Vendt, R., Dalpiaz, F., Brinkkemper, S. (2022). A Semi-automated Method for Domain-Specific Ontology Creation from Medical Guidelines. In: Augusto, A., Gill, A., Bork, D., Nurcan, S., Reinhartz-Berger, I., Schmidt, R. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2022 2022. Lecture Notes in Business Information Processing, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-031-07475-2_20

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  • DOI: https://doi.org/10.1007/978-3-031-07475-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07474-5

  • Online ISBN: 978-3-031-07475-2

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