• About
    • Login
    View Item 
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    •   Institutional Repository Home
    • Electronic Theses and Dissertations
    • Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDepartmentThis CollectionBy Issue DateAuthorsTitlesSubjectsDepartment

    My Account

    LoginRegister

    Evaluation of a novel terminology to categorize clinical document section headers and a related clinical note section tagger

    Denny, Joshua C
    : https://etd.library.vanderbilt.edu/etd-07272007-173034
    http://hdl.handle.net/1803/13640
    : 2007-08-03

    Abstract

    The aims of this project are to 1) build and evaluate a terminology that provides categorization labels, or tags, for common segments within clinical documents, and 2) to evaluate a tool to parse and label natural-language clinical documents using the terminology. Clinical documents generally contain many sections and subsections, such as “history of present illness,” “physical examination,” and “cardiovascular exam.” The author developed a section header terminology that models common section names, subsection names, and their relationships. This terminology was built using existing standardized terminologies, textbooks, and review of over 9,000 clinical notes. The section tagging tool, named SecTag, identifies terminology matches from clinical documents using a combination of linguistic, natural language processing, and machine learning techniques. The evaluation study focused on recognizing sections in 319 randomly-chosen “history and physical examination” notes that were generated during hospitalizations and outpatient visits. The overall recall and precision were 99% and 96%, respectively, over 16,036 possible sections. Recall and precision for sections not labeled in the document were 97% and 87%, respectively. The system correctly tagged 93% of the section start and end boundaries. SecTag failed to label 160 sections (1%); only 11 were headings that were absent in the terminology and which should be added to it. SecTag and its terminology are important first steps for understanding clinical notes. Future studies are needed to extend the terminology to other clinical note types and to link SecTag to a more in-depth natural language processing system.
    Show full item record

    Files in this item

    Icon
    Name:
    denny.pdf
    Size:
    673.0Kb
    Format:
    PDF
    View/Open

    This item appears in the following collection(s):

    • Electronic Theses and Dissertations

    Connect with Vanderbilt Libraries

    Your Vanderbilt

    • Alumni
    • Current Students
    • Faculty & Staff
    • International Students
    • Media
    • Parents & Family
    • Prospective Students
    • Researchers
    • Sports Fans
    • Visitors & Neighbors

    Support the Jean and Alexander Heard Libraries

    Support the Library...Give Now

    Gifts to the Libraries support the learning and research needs of the entire Vanderbilt community. Learn more about giving to the Libraries.

    Become a Friend of the Libraries

    Quick Links

    • Hours
    • About
    • Employment
    • Staff Directory
    • Accessibility Services
    • Contact
    • Vanderbilt Home
    • Privacy Policy