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Detecting Temporal Precursor Words and Phrases Using a Learning Algorithm and Wavelet Analysis

RMost research on mammography focuses on image data, not textual reports.

However, the reports associated with patient visits offer a valuable set of observations.

To take advantage of these sequential writings, a robust ORNL learning algorithm assembles, searches, and analyzes cue phrases in radiology reports to determine if they define normal or abnormal traits in mammograms over time.

Specifically, this system learns phrase patterns (skip bigrams) from textual documents and separates the documents into two distinct classes. The algorithm then performs longitudinal scans of mammogram records from patient visits, using the phrase patterns and a new wavelet analysis technique to detect precursors to breast abnormalities. Using this method, researchers found common phrases in both the normal and abnormal reports and were able to successfully detect common phrase patterns that uniquely identify two classes of documents.

In a follow-up system, the researchers combined the textual analysis algorithm with a discrete wavelet transform—a function in mathematics—to do a temporal analysis of precursor words in medical records. A critical feature of this method is that it will not only identify frequencies in a sequence, but also the point in time in which they occur.

Computational Sciences and Engineering Division
Oak Ridge National Laboratory
Technology Commercialization Manager, Building, Computational, and Transportation Sciences
Oak Ridge National Laboratory
Phone: 865. 241.3808
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