NegBio: a high-performance tool for negation and uncertainty detection in radiology reports

TL;DR

NegBio is a high-performance algorithm that accurately detects negation and uncertainty in radiology reports using dependency patterns, outperforming existing systems across multiple datasets.

Contribution

The paper introduces NegBio, a novel dependency pattern-based method for negation and uncertainty detection in radiology reports, improving accuracy over prior rule-based approaches.

Findings

NegBio achieves 9.5% higher precision than NegEx.

NegBio demonstrates high accuracy across diverse datasets.

The method effectively identifies negation and uncertainty in clinical texts.

Abstract

Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction. Here, we propose a new algorithm, NegBio, to detect negative and uncertain findings in radiology reports. Unlike previous rule-based methods, NegBio utilizes patterns on universal dependencies to identify the scope of triggers that are indicative of negation or uncertainty. We evaluated NegBio on four datasets, including two public benchmarking corpora of radiology reports, a new radiology corpus that we annotated for this work, and a public corpus of general clinical texts. Evaluation on these datasets demonstrates that NegBio is highly accurate for detecting negative and uncertain findings and compares favorably to a widely-used state-of-the-art system NegEx (an average of 9.5% improvement in precision and 5.1% in F1-score).