ArticleEmergency radiology2019
A natural language processing algorithm to extract characteristics of subdural hematoma from head CT reports.
Article in Emergency radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Comparative performance of large language models in structuring head CT radiology reports: multi-institutional validation study in Japan.Japanese journal of radiology · 2025Article
- Article
- How Natural Language Processing Can Aid With Pulmonary Oncology Tumor Node Metastasis Staging From Free-Text Radiology Reports: Algorithm Development and Validation.JMIR formative research · 2023Article
- Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care.Critical care explorations · 2021Article
- Prediction of severe chest injury using natural language processing from the electronic health record.Injury · 2021Article
- Artificial intelligence in emergency medicine: A scoping review.Journal of the American College of Emergency Physicians open · 2020Article
- Natural History of Infratentorial Intracerebral Hemorrhages: Two Subgroups with Distinct Presentations and Outcomes.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2020Observational
- Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2020Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
purposeSubdural hematoma (SDH) is the most common form of traumatic intracranial hemorrhage, and radiographic characteristics of SDH are predictive of complications and patient outcomes. We created a natural language processing (NLP) algorithm to extract structured data from cranial computed tomography (CT) scan reports for patients with SDH.
methodsCT scan reports from patients with SDH were collected from a single center. All reports were based on cranial CT scan interpretations by board-certified attending radiologists. Reports were then coded by a pair of physicians for four variables: number of SDH, size of midline shift, thickness of largest SDH, and side of largest SDH. Inter-rater reliability was assessed. The annotated reports were divided into training (80%) and test (20%) datasets. Relevant information was extracted from text using a pattern-matching approach, due to the lack of a mention-level gold-standard corpus. Then, the NLP pipeline components were integrated using the Apache Unstructured Information Management Architecture. Output performance was measured as algorithm accuracy compared to the data coded by the two ED physicians.
resultsA total of 643 scans were extracted. The NLP algorithm accuracy was high: 0.84 for side of largest SDH, 0.88 for thickness of largest SDH, and 0.92 for size of midline shift.
conclusionA NLP algorithm can structure key data from non-contrast head CT reports with high accuracy. The NLP is a potential tool to detect important radiographic findings from electronic health records, and, potentially, add decision support capabilities.
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