ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018
NegBio: a high-performance tool for negation and uncertainty detection in radiology reports.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed.
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- Context-Aware Sentence Classification of Radiology Reports Using Synthetic Data: Development and Validation Study.Journal of medical Internet research · 2026Article
- Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.Scientific reports · 2026Article
- MetaChest: generalized few-shot learning of pathologies from chest X-rays.Visual computing for industry, biomedicine, and art · 2026Article
- RADEX: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports.European radiology · 2025Article
- A Large Language Model to Detect Negated Expressions in Radiology Reports.Journal of imaging informatics in medicine · 2025Article
- Designing a computer-assisted diagnosis system for cardiomegaly detection and radiology report generation.PLOS digital health · 2025Article
- Automated Radiology Report Labeling in Chest X-Ray Pathologies: Development and Evaluation of a Large Language Model Framework.JMIR medical informatics · 2025Article
- An empirical study of using radiology reports and images to improve intensive care unit mortality prediction.JAMIA open · 2025Article
- AXpert: human expert facilitated privacy-preserving large language models for abdominal X-ray report labeling.JAMIA open · 2025Article
- Enhancing Disease Detection in Radiology Reports Through Fine-tuning Lightweight LLM on Weak Labels.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: A data-driven approach for improved classification.Medical image analysis · 2025Article
- Annotation-free multi-organ anomaly detection in abdominal CT using free-text radiology reports: a multi-centre retrospective study.EBioMedicine · 2024Article
- Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining.Cell reports. Medicine · 2024Article
- How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for Analyses.ArXiv · 2024Article
- Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images.EBioMedicine · 2024Article
- Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images.European radiology experimental · 2024Article
- Modeling disagreement in automatic data labeling for semi-supervised learning in Clinical Natural Language Processing.Frontiers in artificial intelligence · 2024Article
- Vision-language models for medical report generation and visual question answering: a review.Frontiers in artificial intelligence · 2024Review
8 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
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). AVAILABILITY: https://github.com/ncbi-nlp/NegBio.
Identifiers
29888070PMC5961822What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.