"Radiology notes text analysis"

DocMusherDocMusher Member Posts: 333 Unicorn
edited May 2019 in Help


Radiology is a hot topic in the AI scene. Both image and text analysis are part of this process. I liked this paper as part of the text analysis quest. 

Interested in your feedbacks.




  • DocMusherDocMusher Member Posts: 333 Unicorn
    edited November 2018

    Hi in the context of text analysis of clinical reports I found also;

    If you know NLP, ConText is based on a negation algorithm called NegEx. ConText's input is a sentence with indexed clinical conditions; ConText's output for each indexed condition is the value for contextual features or modifiers. The initial versions of ConText determines values for three modifiers - Negation: affirmed or negated. Temporality: recent, historical, or hypothetical. Experiencer: patient or other.

    A newer version (pyConText) is more extensible and can have user-defined modifiers, One project involving radiology reports added the following modifiers: Uncertainty: certain or uncertain. Quality of radiologic exam: limited or not limited. Severity: critical or non-critical. Sidedness: right or left as well as others.

    The google code site contains java and python versions of ConText and NegEx, links to papers describing and evaluating the algorithms, a description of the algorithm (including a list of the trigger terms used for each type of modifier), and a dataset of 120 reports of six types with manually-assigned values to the three modifiers in the original version of ConText. Some ConText trigger terms have translations for Swedish, German, and French.




  • DocMusherDocMusher Member Posts: 333 Unicorn

     Hi in the context of text analysis of clinical reports I found also: http://blulab.chpc.utah.edu/content/contextnegex



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