ConText: an algorithm for identifying contextual features from clinical text

Authors:
Wendy W. Chapman;David Chu;John N. Dowling
Affiliations:
University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA
Venue:
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Year:
2007

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Abstract

Applications using automatically indexed clinical conditions must account for contextual features such as whether a condition is negated, historical or hypothetical, or experienced by someone other than the patient. We developed and evaluated an algorithm called ConText, an extension of the NegEx negation algorithm, which relies on trigger terms, pseudo-trigger terms, and termination terms for identifying the values of three contextual features. In spite of its simplicity, ConText performed well at identifying negation and hypothetical status. ConText performed moderately at identifying whether a condition was experienced by someone other than the patient and whether the condition occurred historically.