A Novel Hybrid Approach to Automated Negation Detection in Clinical Radiology Reports
- Affiliations of the authors: Stanford Medical Informatics and Stanford Center for Clinical Informatics, Stanford University School of Medicine, Stanford, California
- Correspondence and reprints: Yang Huang, PhD, Stanford Medical Informatics, MSOB X215, 251 Campus Drive, Stanford, CA 94305-5479 e-mail: <huangy{at}stanford.edu>
- Received 18 September 2006
- Accepted 29 January 2007
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.
Footnotes
-
We are very grateful to the reviewers, Nigam Shah, MD, Todd Ferris, MD, Mia Levy, MD, and Cesar Rodriguez, MD, for their hard work and great patience. We also thank Dr. Wendy Chapman for publishing the algorithm of NegEx online and promptly answering our questions on NegEx.
