By Cynthia E. Keen, staff writer
    November 16, 2012

    Thursday, November 29 | 11:30 a.m.-11:40 a.m. | SSQ10-07 | Room S403A
    In this scientific session, researchers will describe how a natural language processing (NLP) tool can identify "missing" core semantic elements in reports assessing bone tumors on radiographs. Such a tool used along with speech recognition could provide real-time feedback and help improve the quality of reports.

    One of the arguments made for adopting structured report templates is that they help radiologists from overlooking important elements, especially for reports that are complex. Dr. Bao Do, a clinical instructor at Stanford University Medical Center, and colleagues decided to determine whether an NLP tool could alert a radiologist to elements that should be included in a report but were missing at the conclusion of a dictation using a speech recognition system.

    In their experiment, they developed and validated an NLP tool to extract core features in assessing bone lesions on an x-ray image. The tool was then used to process 47 unstructured reports and categorize omissions by semantic categories. The tool correctly categorized 94%.

    What are they going to do next? Attend this session to find out.

    Last Updated np 11/15/2012 3:59:28 PM