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

arXiv: https://arxiv.org/abs/1712.05898 · HTML: https://mlsanity.com/paper/1712.05898/

## Key facts

- Cited by 79 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2017.

## Built on by (most cited first, 30 of 79)

- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology](https://mlsanity.com/paper/2301.02228/) (2023)
- [Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images](https://mlsanity.com/paper/2302.14042/) (2023)
- [Vision-Language Models for Medical Report Generation and Visual Question Answering: A Review](https://mlsanity.com/paper/2403.02469/) (2024)
- [VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge](https://mlsanity.com/paper/2411.12915/) (2024)
- [Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study](https://mlsanity.com/paper/2208.13365/) (2022)
- [Progressive Transformer-Based Generation of Radiology Reports](https://mlsanity.com/paper/2102.09777/) (2021)
- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (2023)
- [RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting](https://mlsanity.com/paper/2107.02104/) (2021)
- [VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels](https://mlsanity.com/paper/2102.11467/) (2021)
- [Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation](https://mlsanity.com/paper/2210.03256/) (2022)
- [Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays](https://mlsanity.com/paper/2206.12704/) (2022)
- [BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis](https://mlsanity.com/paper/2108.04938/) (2021)
- [Medical Image Captioning via Generative Pretrained Transformers](https://mlsanity.com/paper/2209.13983/) (2022)
- [Improving negation detection with negation-focused pre-training](https://mlsanity.com/paper/2205.04012/) (2022)
- [MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks](https://mlsanity.com/paper/2309.14118/) (2023)
- [A Systematic Review of Deep Learning-based Research on Radiology Report Generation](https://mlsanity.com/paper/2311.14199/) (2023)
- [Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding](https://mlsanity.com/paper/2204.03035/) (2022)
- [Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training](https://mlsanity.com/paper/2307.07246/) (2023)
- [UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training](https://mlsanity.com/paper/2309.06828/) (2023)
- [RadGraph2: Modeling Disease Progression in Radiology Reports via Hierarchical Information Extraction](https://mlsanity.com/paper/2308.05046/) (2023)
- [Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images](https://mlsanity.com/paper/2308.07688/) (2023)
- [Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performance](https://mlsanity.com/paper/2310.09507/) (2023)
- [A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing](https://mlsanity.com/paper/2112.05780/) (2021)
- [Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels](https://mlsanity.com/paper/2211.13606/) (2022)
- [CheX-GPT: Harnessing Large Language Models for Enhanced Chest X-ray Report Labeling](https://mlsanity.com/paper/2401.11505/) (2024)
- [Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning](https://mlsanity.com/paper/2402.03783/) (2024)
- [X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation](https://mlsanity.com/paper/2406.17911/) (2024)
- [Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification](https://mlsanity.com/paper/2403.04024/) (2024)

## Abstract (arXiv)

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).