NegBio: a high-performance tool for negation and uncertainty detection in radiology reports (1712.05898v2)

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).

• The paper introduces NegBio, a tool that significantly improves negation and uncertainty detection in radiology reports using universal dependency graphs.
• It demonstrates superior performance with up to 94.4% precision, outperforming traditional methods like NegEx across multiple datasets.
• The study underscores the value of syntactic analysis in NLP, paving the way for more accurate clinical data extraction and improved diagnostic workflows.

Analyzing NegBio: A Tool for Enhanced Detection of Negation and Uncertainty in Radiology Reports

This paper introduces NegBio, a sophisticated tool designed to ameliorate challenges in the detection of negation and uncertainty in radiology reports. The accurate discernment of negative and uncertain findings is essential for effective information extraction, particularly given the prevalence of such results in radiology documentation. Historically, rule-based methods such as NegEx have been the standard approach for this task, but they possess limitations in handling complex syntactic structures. NegBio addresses these challenges by leveraging universal dependency patterns, allowing for more nuanced recognition of negation and uncertainty.

Methodology

NegBio operates by constructing a universal dependency graph (UDG) for each sentence. This graph is used to identify the syntactic relationships between words, utilizing pattern matching to detect negation and uncertainty. This approach moves beyond surface-level regular expressions, offering a more structured analysis of the text. To evaluate the tool, the authors compared it against NegEx across multiple datasets, including public corpora like OpenI, ChestX-ray, BioScope, and PK.

Results

NegBio demonstrates notable improvements over NegEx in terms of precision and F1-score. In evaluations on OpenI and ChestX-ray datasets, NegBio achieved precision scores of 89.8% and 94.4%, respectively, representing a significant increase from the scores observed when using NegEx. Similarly, in detecting negated expressions within the BioScope and PK datasets, NegBio maintained higher precision levels than NegEx, showing a precision increase of 25.5% on BioScope. The improvement across different datasets underscores the generalizability and robustness of NegBio.

Implications and Future Directions

The introduction of NegBio has practical implications for the field of radiology and healthcare informatics at large. By enhancing the precision of information extraction systems, it holds the potential to improve diagnostic workflows and data management in clinical settings. The tool's reliance on syntactic parsing rather than keyword matching suggests a shift towards more context-aware NLP tools.

Theoretically, NegBio exemplifies how integrating syntactical understanding into NLP tasks can lead to significant performance enhancements. This indicates a fruitful area of research, advocating for the application of similar techniques to other domains within clinical NLP.

Despite these advancements, the authors recognize the need for further development. Issues such as parsing errors in complex sentence structures and the challenge of recognizing double negations remain. Future work might involve refining the dependency patterns within NegBio and extending its applicability to broader types of clinical texts beyond radiology reports.

In conclusion, NegBio represents an important step forward in the detection of negation and uncertainty in medical documentation. The authors not only contribute a valuable tool for immediate use but also establish a framework for future innovations in the application of syntactic analysis to NLP tasks in the healthcare industry.