ArticleJournal of biomedical semantics2019
Text mining brain imaging reports.
Article in Journal of biomedical semantics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Natural Language Processing Model in Extracting Information from Free-Text Radiology Reports: A Systematic Review and Meta-Analysis.Journal of imaging informatics in medicine · 2026Pooled it
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Artificial Intelligence in Predicting Surgical Problems and Postoperative Morbidity in Mandibular Third Molar Extractions.Annals of African medicine · 2026Article
- Clinically reported covert cerebrovascular disease and risk of neurological disease: a whole-population cohort of 367 988 people using natural language processing.Journal of neurology, neurosurgery, and psychiatry · 2026Article
- A large dataset of brain imaging linked to health systems data: curation and access to a whole system national cohort from NHS Scotland.GigaScience · 2026Article
- Natural Language Processing to Automate Cerebrovascular Event Identification in Stroke Alerts.Stroke (Hoboken, N.J.) · 2025Article
- Dementia-related volumetric assessments in neuroradiology reports: a natural language processing-based study.BMJ open · 2025Article
- Fine-tuning of language models for automated structuring of medical exam reports to improve patient screening and analysis.Scientific reports · 2025Article
- A Large Language Model to Detect Negated Expressions in Radiology Reports.Journal of imaging informatics in medicine · 2025Article
- Enhancing predictive analytics in mandibular third molar extraction using artificial intelligence: A CBCT-Based study.The Saudi dental journal · 2024Article
- TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines.BMC medical informatics and decision making · 2024Article
- Named Entity Recognition in Electronic Health Records: A Methodological Review.Healthcare informatics research · 2023Article
- Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports.Frontiers in digital health · 2023Article
- Automated clinical coding: what, why, and where we are?NPJ digital medicine · 2022Review
- Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports.JAMA network open · 2022Article
- Labeling Noncontrast Head CT Reports for Common Findings Using Natural Language Processing.AJNR. American journal of neuroradiology · 2022Article
- Construction of a Legal System of Corporate Social Responsibility Based on Big Data Analysis Technology.Journal of environmental and public health · 2022Article
- Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke.BMC medical informatics and decision making · 2021Article
- Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports.Journal of healthcare engineering · 2021Article
- 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
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6 authors.
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Abstract
backgroundWith the improvements to text mining technology and the availability of large unstructured Electronic Healthcare Records (EHR) datasets, it is now possible to extract structured information from raw text contained within EHR at reasonably high accuracy. We describe a text mining system for classifying radiologists' reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations. Our system, the Edinburgh Information Extraction for Radiology reports (EdIE-R) system, which we describe here, was developed and tested on a collection of radiology reports.The work reported in this paper is based on 1168 radiology reports from the Edinburgh Stroke Study (ESS), a hospital-based register of stroke and transient ischaemic attack patients. We manually created annotations for this data in parallel with developing the rule-based EdIE-R system to identify phenotype information related to stroke in radiology reports. This process was iterative and domain expert feedback was considered at each iteration to adapt and tune the EdIE-R text mining system which identifies entities, negation and relations between entities in each report and determines report-level labels (phenotypes).
resultsThe inter-annotator agreement (IAA) for all types of annotations is high at 96.96 for entities, 96.46 for negation, 95.84 for relations and 94.02 for labels. The equivalent system scores on the blind test set are equally high at 95.49 for entities, 94.41 for negation, 98.27 for relations and 96.39 for labels for the first annotator and 96.86, 96.01, 96.53 and 92.61, respectively for the second annotator.
conclusionAutomated reading of such EHR data at such high levels of accuracies opens up avenues for population health monitoring and audit, and can provide a resource for epidemiological studies. We are in the process of validating EdIE-R in separate larger cohorts in NHS England and Scotland. The manually annotated ESS corpus will be available for research purposes on application.
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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.