ArticleNeurocritical care2022
Natural Language Processing of Radiology Reports to Detect Complications of Ischemic Stroke.
Article in Neurocritical care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 23 citations in OpenAlex.
- Artificial Intelligence Across the Radiology Workflow: A Nine-Stage Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions.Stroke (Hoboken, N.J.) · 2026Review
- RehabMate: an explainable framework for action detection and corrective feedback in pediatric stroke rehabilitation.Scientific reports · 2026Article
- Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke.NPJ digital medicine · 2025Article
- Data transformation of unstructured electroencephalography reports by natural language processing: improving data usability for large-scale epilepsy studies.Frontiers in neurology · 2025Article
- AI-driven integration of imaging and radiology language improves stroke mortality prediction.Frontiers in neurology · 2025Article
- Natural language processing in the intensive care unit: A scoping review.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2024Article
- Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review.JMIR neurotechnology · 2024Review
- Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review.JMIR medical informatics · 2023Article
- Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing.Brain informatics · 2023Article
- Artificial Intelligence to Improve Patient Understanding of Radiology Reports.The Yale journal of biology and medicine · 2023Review
- Machine Learning in Clinical Trials: A Primer with Applications to Neurology.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Navigating the Ocean of Big Data in Neurocritical Care.Neurocritical care · 2022Article
- Machine learning-based prediction of cerebral artery territorial infarcts in acute ischemic stroke using non-contrast CT.Frontiers in neurologyArticle
- A multimodal deep learning model for predicting early neurological deterioration in patients with acute ischemic stroke.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
20 authors at 10 institutions in 3 countries.
Funding
Abstract
backgroundAbstraction of critical data from unstructured radiologic reports using natural language processing (NLP) is a powerful tool to automate the detection of important clinical features and enhance research efforts. We present a set of NLP approaches to identify critical findings in patients with acute ischemic stroke from radiology reports of computed tomography (CT) and magnetic resonance imaging (MRI).
methodsWe trained machine learning classifiers to identify categorical outcomes of edema, midline shift (MLS), hemorrhagic transformation, and parenchymal hematoma, as well as rule-based systems (RBS) to identify intraventricular hemorrhage (IVH) and continuous MLS measurements within CT/MRI reports. Using a derivation cohort of 2289 reports from 550 individuals with acute middle cerebral artery territory ischemic strokes, we externally validated our models on reports from a separate institution as well as from patients with ischemic strokes in any vascular territory.
resultsIn all data sets, a deep neural network with pretrained biomedical word embeddings (BioClinicalBERT) achieved the highest discrimination performance for binary prediction of edema (area under precision recall curve [AUPRC] > 0.94), MLS (AUPRC > 0.98), hemorrhagic conversion (AUPRC > 0.89), and parenchymal hematoma (AUPRC > 0.76). BioClinicalBERT outperformed lasso regression (p < 0.001) for all outcomes except parenchymal hematoma (p = 0.755). Tailored RBS for IVH and continuous MLS outperformed BioClinicalBERT (p < 0.001) and linear regression, respectively (p < 0.001).
conclusionsOur study demonstrates robust performance and external validity of a core NLP tool kit for identifying both categorical and continuous outcomes of ischemic stroke from unstructured radiographic text data. Medically tailored NLP methods have multiple important big data applications, including scalable electronic phenotyping, augmentation of clinical risk prediction models, and facilitation of automatic alert systems in the hospital setting.
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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.