Automated Detection of Ambiguity in BI-RADS Assessment Categories in Mammography Reports

Abstract

An unsolved challenge in biomedical natural language processing (NLP) is detecting ambiguities in the reports that can help physicians to improve report clarity. Our goal was to develop NLP methods to tackle the challenges of identifying ambiguous descriptions of the laterality of BI-RADS Final Assessment Categories in mammography radiology reports. We developed a text processing system that uses a BI-RADS ontology we built as a knowledge source for automatic annotation of the entities in mammography reports relevant to this problem. We used the GATE NLP toolkit and developed customized processing resources for report segmentation, named entity recognition, and detection of mismatches between BI-RADS Final Assessment Categories and mammogram laterality. Our system detected 55 mismatched cases in 190 reports and the accuracy rate was 81%. We conclude that such NLP techniques can detect ambiguities in mammography reports and may reduce discrepancy and variability in reporting.