ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports

By H. Harkema, J. Dowling, Tyler Thornblade, Wendy W. Chapman. Journal of Biomedical Informatics, 2009

407 citations recorded by the source corpus.

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Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports (2015, methodology): “Third, Topaz uses the ConText algorithm [23] to determine who experienced the finding (e.”
NegAIT: A new parser for medical text simplification using morphological, sentential and double negation (2017, methodology): “ConText [37] is also an extension of NegEx to determine whether clinical conditions are negated, hypothetical, historical or experienced by someone other than patients.”
Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks (2010, methodology): “ical (Harkema et al., 2009).”
Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. (2011, methodology): “They tested two information extraction methods: Dynamic-window and ConText [15] for building cancer frames, which showed that both the methods performed much better than their human benchmark.”
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports (2017, methodology): “NegEx is a widely used algorithm that utilizes regular expressions 7,8 .”