ArticleJMIR AI2023
Extraction of Radiological Characteristics From Free-Text Imaging Reports Using Natural Language Processing Among Patients With Ischemic and Hemorrhagic Stroke: Algorithm Development and Validation.
Article in JMIR AI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline 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
- Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions.Stroke (Hoboken, N.J.) · 2026Review
- AI-driven integration of imaging and radiology language improves stroke mortality prediction.Frontiers in neurology · 2025Article
- Artificial intelligence, machine learning, and reproducibility in stroke research.European stroke journal · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
backgroundNeuroimaging is the gold-standard diagnostic modality for all patients suspected of stroke. However, the unstructured nature of imaging reports remains a major challenge to extracting useful information from electronic health records systems. Despite the increasing adoption of natural language processing (NLP) for radiology reports, information extraction for many stroke imaging features has not been systematically evaluated.
objectiveIn this study, we propose an NLP pipeline, which adopts the state-of-the-art ClinicalBERT model with domain-specific pretraining and task-oriented fine-tuning to extract 13 stroke features from head computed tomography imaging notes.
methodsWe used the model to generate structured data sets with information on the presence or absence of common stroke features for 24,924 patients with strokes. We compared the survival characteristics of patients with and without features of severe stroke (eg, midline shift, perihematomal edema, or mass effect) using the Kaplan-Meier curve and log-rank tests.
resultsPretrained on 82,073 head computed tomography notes with 13.7 million words and fine-tuned on 200 annotated notes, our HeadCT_BERT model achieved an average area under receiver operating characteristic curve of 0.9831, F
conclusionsOur proposed NLP pipeline achieved high performance and has the potential to improve medical research and patient safety.
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Registered trials
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