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Detecting Intuitive Mentions of Diseases in Narrative Clinical Text

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Book cover Artificial Intelligence in Medicine (AIME 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5651))

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Abstract

A significant portion of the clinical information content of narrative text documents in the medical record is only mentioned intuitively, but automated information extraction systems typically focus on explicitly mentioned concepts only. To extend the extraction of clinical information to intuitively mentioned diseases, we have developed a natural language processing application based on MMTx and on context analysis algorithms, enhanced with the detection of disease-specific concepts (e.g. medications used only for this disease), and values of some specific biomarkers. This application was developed for the i2b2 obesity challenge, a competition focused on the detection of patients with obesity or common comorbidities.

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Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA

    Stéphane M. Meystre

Authors
  1. Stéphane M. Meystre
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Editor information

Editors and Affiliations

  1. University of Verona, Department of Computer Science, Ca’ Vignal 2, strada le Grazie 15, 37134, Verona, Italy

    Carlo Combi

  2. Department of Information Systems Engineering, Ben Gurion University of the Negev, P.O. Box 653, 84105, Beer-Sheva, Israel

    Yuval Shahar

  3. Department of Medical Informatics, University of Amsterdam, Academic Medical Center, Meibergdreef 15, 1105, Amsterdam, AZ, The Netherlands

    Ameen Abu-Hanna

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Meystre, S.M. (2009). Detecting Intuitive Mentions of Diseases in Narrative Clinical Text. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_31

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  • DOI: https://doi.org/10.1007/978-3-642-02976-9_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02975-2

  • Online ISBN: 978-3-642-02976-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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