NegBio: a high-performance tool for negation and uncertainty detection in radiology reports
Explore this paper's citation graph
Summary
Evaluation on four 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.
- Type
- preprint
- Published
- 2017-12-16
- Cited by
- 233
- References
- 34
- Access
- Open access
- OpenAlex
- https://openalex.org/W2778310824
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:19572090
Keywords
Negation, Benchmarking, Computer science, Scope (computer science), Radiology
References
- MetaMap Lite: an evaluation of a new Java implementation of MetaMap
- Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing
- Descriptive Analysis of Negation Cues in Biomedical Texts
- Dependency Parser-based Negation Detection in Clinical Narratives
- Extending the NegEx Lexicon for Multiple Languages
- MITRE system for clinical assertion status classification
- Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit
- Representing information in patient reports using natural language processing and the extensible markup language.
- Speculation and negation annotation in natural language texts: what the case of BioScope might (not) reveal
- Evaluation of negation phrases in narrative clinical reports
- Universal Stanford dependencies: A cross-linguistic typology
- Context: An Algorithm for Determining Negation, Experiencer, and Temporal Status from Clinical Reports
- Interrater reliability: the kappa statistic
- Learning Alignments and Leveraging Natural Logic
- Document-Level Classification of CT Pulmonary Angiography Reports based on an Extension of the ConText Algorithm
- Approximate Subgraph Matching-Based Literature Mining for Biomedical Events and Relations
- DEEPEN: A negation detection system for clinical text incorporating dependency relation into NegEx
- Evaluation of Negation and Uncertainty Detection and its Impact on Precision and Recall in Search
- A novel hybrid approach to automated negation detection in clinical radiology reports.
- An overview of MetaMap: historical perspective and recent advances
Cited by
- Opportunities and obstacles for deep learning in biology and medicine
- Détection de la négation : corpus français et apprentissage supervisé
- Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks
- Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction
- CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
- End-to-end Joint Entity Extraction and Negation Detection for Clinical Text
- Actes de la conférence Traitement Automatique de la Langue Naturelle, TALN 2018
- PadChest: A large chest x-ray image dataset with multi-label annotated reports
- Portée de la négation : détection par apprentissage supervisé en français et portugais brésilien (Negation scope : sequence labeling by supervised learning in French and Brazilian-Portuguese)
- Cross-Modal Data Programming Enables Rapid Medical Machine Learning
- Clinically Accurate Chest X-Ray Report Generation
- Caveats in Generating Medical Imaging Labels from Radiology Reports
- Classifying the reported ability in clinical mobility descriptions
- Joint Entity Extraction and Assertion Detection for Clinical Text
- Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction from chest X-ray reports using deep learning
- Speculation and Negation detection in French biomedical corpora
- Learning to estimate label uncertainty for automatic radiology report parsing
- Negation detection in Norwegian medical text : Porting a Swedish NegEx to Norwegian. Work in progress
- Automated analysis of radiology images using Convolutional Neural Networks
- NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution
Related papers
- PadChest: A large chest x-ray image dataset with multi-label annotated reports
- A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries
- Prioritization of Free-Text Clinical Documents: A Novel Use of a Bayesian Classifier
- tbiExtractor: A framework for extracting traumatic brain injury common data elements from radiology reports
- Identification of Long Bone Fractures in Radiology Reports Using Natural Language Processing to support Healthcare Quality Improvement
- Deep-Learning Language-Modeling Approach for Automated, Personalized, and Iterative Radiology-Pathology Correlation.
- Clinically Accurate Chest X-Ray Report Generation
- Detection of unexpected findings in radiology reports: A comparative study of machine learning approaches