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