Dependency Parser-based Negation Detection in Clinical Narratives

Negation of clinical named entities is common in clinical documents and is a crucial factor to accurately compile patients’ clinical conditions and to further support complex phenotype detection. In 2009, Mayo Clinic released the clinical Text Analysis and Knowledge Extraction System (cTAKES), which includes a negation annotator that identifies negation status of a named entity by searching for negation words within a fixed word distance. However, this negation strategy is not sophisticated enough to correctly identify complicated patterns of negation. This paper aims to investigate whether the dependency structure from the cTAKES dependency parser can improve the negation detection performance. Manually compiled negation rules, derived from dependency paths were tested. Dependency negation rules do not limit the negation scope to word distance; instead, they are based on syntactic context. We found that using a dependency-based negation proved a superior alternative to the current cTAKES negation annotator.