ArticleJournal of digital imaging2017
Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing.
Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Large language model ensemble for automated TNM staging from radiology reports.Bioinformatics (Oxford, England) · 2026Article
- Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- [Transformation of free-text radiology reports into structured data].Radiologie (Heidelberg, Germany) · 2025Review
- Comparison of natural language processing algorithms in assessing the importance of head computed tomography reports written in Japanese.Japanese journal of radiology · 2024Article
- Review
- Artificial Intelligence to Improve Patient Understanding of Radiology Reports.The Yale journal of biology and medicine · 2023Review
- Natural language processing for automated annotation of medication mentions in primary care visit conversations.JAMIA open · 2021Article
- Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework.Journal of medical Internet research · 2021Article
- Natural language processing of radiology reports for the identification of patients with fracture.Archives of osteoporosis · 2021Article
- Artificial intelligence in paediatric radiology: Future opportunities.The British journal of radiology · 2021Review
- Multi-Ontology Refined Embeddings (MORE): A hybrid multi-ontology and corpus-based semantic representation model for biomedical concepts.Journal of biomedical informatics · 2020Article
- Comparison of deep learning models for natural language processing-based classification of non-English head CT reports.Neuroradiology · 2020Article
- A dataset of chest X-ray reports annotated with Spatial Role Labeling annotations.Data in brief · 2020Article
- A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning.Applied clinical informatics · 2020Article
- Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction from chest X-ray reports using deep learning.Journal of biomedical informatics · 2020Article
- Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification.Journal of digital imaging · 2020Article
- tbiExtractor: A framework for extracting traumatic brain injury common data elements from radiology reports.PloS one · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We built a natural language processing (NLP) method to automatically extract clinical findings in radiology reports and characterize their level of change and significance according to a radiology-specific information model. We utilized a combination of machine learning and rule-based approaches for this purpose. Our method is unique in capturing different features and levels of abstractions at surface, entity, and discourse levels in text analysis. This combination has enabled us to recognize the underlying semantics of radiology report narratives for this task. We evaluated our method on radiology reports from four major healthcare organizations. Our evaluation showed the efficacy of our method in highlighting important changes (accuracy 99.2%, precision 96.3%, recall 93.5%, and F1 score 94.7%) and identifying significant observations (accuracy 75.8%, precision 75.2%, recall 75.7%, and F1 score 75.3%) to characterize radiology reports. This method can help clinicians quickly understand the key observations in radiology reports and facilitate clinical decision support, review prioritization, and disease surveillance.
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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.