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Semi-automated Ontology Development and Management System Applied to Medically Unexplained Syndromes in the U.S. Veterans Population

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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

Terminologies or ontologies to describe patient-reported information are lacking. The development and maintenance of ontologies is usually a manual, lengthy, and resource-intensive process. To support the development of medical specialty-specific ontologies, we created a semi-automated ontology development and management system (SEAM). SEAM supports ontology development by automatically extracting terms, concepts, and relations from narrative text, and then offering a streamlined graphical user interface to edit and create content in the ontology and finally export it in OWL format. The graphical user interface implements card sorting for synonym grouping and concept laddering for hierarchy construction. We used SEAM to create ontologies to support medically unexplained syndromes detection and management among veterans in the U.S.

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Acknowledgments

We thank Yarden Livnat and Kristin Potter for their work with the frontend graphical user interface. Project funded under VA HSR&D contract 11RT0150. SEAM is available at http://kdh-nlp.org/Seam-project/seam-home.html.

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Authors and Affiliations

  1. Medical University of South Carolina, Charleston, SC, USA

    Stéphane M. Meystre

  2. Westminster College, Salt Lake City, UT, USA

    Kristina Doing-Harris

Authors
  1. Stéphane M. Meystre
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  2. Kristina Doing-Harris
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Corresponding author

Correspondence to Stéphane M. Meystre .

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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Meystre, S.M., Doing-Harris, K. (2017). Semi-automated Ontology Development and Management System Applied to Medically Unexplained Syndromes in the U.S. Veterans Population. 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_41

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  • DOI: https://doi.org/10.1007/978-3-319-59758-4_41

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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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