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Detecting Low Back Pain from Clinical Narratives Using Machine Learning Approaches

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Database and Expert Systems Applications (DEXA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 903))

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Abstract

Free-text clinical notes recorded during the patients’ visits in the Electronic Medical Record (EMR) system narrates clinical encounters, often using ‘SOAP’ notes (an acronym for subject, objective, assessment, and plan). The free-text notes represent a wealth of information for discovering insights, particularly in medical conditions such as pain and mental illness, where regular health metrics provide very little knowledge about the patients’ medical situations and reactions to treatments. In this paper, we develop a generic text-mining and decision support framework to diagnose chronic low back pain. The framework utilizes open-source algorithms for anonymization, natural language processing, and machine learning to classify low back pain patterns from unstructured free-text notes in the Electronic Medical Record (EMR) system as noted by the primary care physicians during patients’ visits. The initial results show a high accuracy for the limited thirty-four patient labelled data set that we used in this pilot study. We are currently processing a larger data set to test our approach.

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Author information

Authors and Affiliations

  1. Queen’s University, Kingston, ON, K7L 2N8, Canada

    Michael Judd, Farhana Zulkernine, Brent Wolfrom, David Barber & Akshay Rajaram

Authors
  1. Michael Judd
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  2. Farhana Zulkernine
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  3. Brent Wolfrom
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  4. David Barber
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  5. Akshay Rajaram
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Corresponding author

Correspondence to Farhana Zulkernine .

Editor information

Editors and Affiliations

  1. University of Tunis, Tunis, Tunisia

    Mourad Elloumi

  2. MiCS, Media Computer Science, University of Passau, Passau, Bayern, Germany

    Michael Granitzer

  3. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Twente, Enschede, Overijssel, The Netherlands

    Christin Seifert

  5. Fak. Medien, Bauhaus Universität Weimar, Weimar, Thüringen, Germany

    Benno Stein

  6. Inst. für Softwaretechnik, Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  7. FAW, Johannes Kepler University of Linz, Linz, Austria

    Roland Wagner

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Judd, M., Zulkernine, F., Wolfrom, B., Barber, D., Rajaram, A. (2018). Detecting Low Back Pain from Clinical Narratives Using Machine Learning Approaches. In: Elloumi, M., et al. Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-319-99133-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-99133-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99132-0

  • Online ISBN: 978-3-319-99133-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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