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

By Yifan Peng, Xiaosong Wang, Le Lu, Mohammadhadi Bagheri, Ronald M. Summers, Zhiyong Lu. 2017

229 citations recorded by the source corpus.

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). Availability: https://github.com/ncbi-nlp/NegBio

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports (2021, methodology): “Large-scale datasets, such as MIMIC-CXR [1] and CheXpert [2], use automated radiology report labelers [2–6] to extract common medical conditions from reports.”
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT (2020, methodology): “NegBio (Peng et al., 2017), an extension to NegEx, utilizes universal dependencies for pattern deﬁnition and subgraph matching for graph traversal search, includes uncertainty detection in addition to negation detection for multiple pathologies in chest x-ray reports, and is used to generate labels…”
MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs (2019, methodology): “Images are also provided with one or more labels derived from the corresponding free-text radiology report using open source labelers [12, 13].”
When Radiology Report Generation Meets Knowledge Graph (2020, methodology): “We adopted a similar method proposed in NegBio (Peng et al. 2018; Wang et al. 2017) and CheXpert (Irvin et al. 2019) labeling toolkit for entity extraction and rule-based negation detection.”
Clinically Accurate Chest X-Ray Report Generation (2019, methodology): “We decide not to include NegBio (Peng et al., 2018), a previous state-of-the-art disease labeling system, due to its significant performance gap with CheXpert as reported Irvin et al.”
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison (2019, methodology): “To obtain the universal dependency parse, we follow a procedure similar to Peng et al.(2018): ﬁrst, the report is split and tokenized into sentences using NLTK (Bird, Klein, and Loper 2009)”
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text (2022, unknown): “Follow the practice of Peng et al. (2018), we focus on 14 main entity types shown by Table 5.”