TITLE

A Novel Hybrid Approach to Automated Negation Detection in Clinical Radiology Reports

AUTHOR(S)
Yang Huang; Lowe, Henry J.
PUB. DATE
May 2007
SOURCE
Journal of the American Medical Informatics Association;May/Jun2007, Vol. 14 Issue 3, p304
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Objective: Negation is common in clinical documents and is an important source of poor precision in automated indexing systems. Previous research has shown that negated terms may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We describe a novel hybrid approach, combining regular expression matching with grammatical parsing, to address the above limitation in automatically detecting negations in clinical radiology reports. Design: Negations are classified based upon the syntactical categories of negation signals, and negation patterns, using regular expression matching. Negated terms are then located in parse trees using corresponding negation grammar. Measurements: A classification of negations and their corresponding syntactical and lexical patterns were developed through manual inspection of 30 radiology reports and validated on a set of 470 radiology reports. Another 120 radiology reports were randomly selected as the test set on which a modified Delphi design was used by four physicians to construct the gold standard. Results: In the test set of 120 reports, there were a total of 2,976 noun phrases, of which 287 were correctly identified as negated (true positives), along with 23 undetected true negations (false negatives) and 4 mistaken negations (false positives). The hybrid approach identified negated phrases with sensitivity of 92.6% (95% CI 90.9-93.4%), positive predictive value of 98.6% (95% CI 96.9-99.4%), and specificity of 99.87% (95% CI 99.7-99.9%). Conclusion: This novel hybrid approach can accurately locate negated concepts in clinical radiology reports not only when in close proximity to, but also at a distance from, negation signals.
ACCESSION #
25250650

 

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