ArticleStroke2021
Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports.
Article in Stroke, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 30 citations in OpenAlex.
- Review
- Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions.Stroke (Hoboken, N.J.) · 2026Review
- Natural language processing for triage of cerebral large-vessel occlusion.Arquivos de neuro-psiquiatria · 2025Article
- Early stroke detection through machine learning in the prehospital setting.Frontiers in cardiovascular medicine · 2025Article
- Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.Journal of the American Heart Association · 2024Article
- Machine Learning to Enhance Electronic Detection of Diagnostic Errors.JAMA network open · 2024Article
- Clinical trauma severity of indoor and outdoor injurious falls requiring emergency medical service response.Injury epidemiology · 2024Article
- Pre-Hospital Stroke Care beyond the MSU.Current neurology and neuroscience reports · 2024Review
- Clinical Trauma Severity of Indoor and Outdoor Injurious Falls Requiring Emergency Medical Service Response.Research square · 2024Article
- Automated Extraction of Stroke Severity from Unstructured Electronic Health Records using Natural Language Processing.medRxiv : the preprint server for health sciences · 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
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- Prehospital stroke management and mobile stroke units.Current opinion in neurology · 2023Review
- Emergency Medical Services Data: An Unexpected Source of Variation in Stroke Care Performance.Journal of the American Heart Association · 2023Article
- Emergency Medical Services Leadership Perspectives on Implementation of Evidence-Based Guidelines: A Qualitative Study.Prehospital emergency care · 2023Article
- Artificial intelligence decision points in an emergency department.Clinical and experimental emergency medicine · 2022Article
- Natural Language Processing of Radiology Reports to Detect Complications of Ischemic Stroke.Neurocritical care · 2022Article
- The Role of Artificial Intelligence in Acute Ischemic Stroke Care: A Narrative Review.Missouri medicineReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 7 institutions in 3 countries.
Funding
Abstract
Background and Purpose: Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. We aimed to develop a model that utilizes natural language processing of EMS reports and machine learning to improve prehospital stroke identification. Methods: We conducted a retrospective study of patients transported by the Chicago EMS to 17 regional primary and comprehensive stroke centers. Patients who were suspected of stroke by the EMS or had hospital-diagnosed stroke were included in our cohort. Text within EMS reports were converted to unigram features, which were given as input to a support-vector machine classifier that was trained on 70% of the cohort and tested on the remaining 30%. Outcomes included final diagnosis of stroke versus nonstroke, large vessel occlusion, severe stroke (National Institutes of Health Stroke Scale score >5), and comprehensive stroke center-eligible stroke (large vessel occlusion or hemorrhagic stroke). Results: Of 965 patients, 580 (60%) had confirmed acute stroke. In a test set of 289 patients, the text-based model predicted stroke nominally better than models based on the Cincinnati Prehospital Stroke Scale (c-statistic: 0.73 versus 0.67, P=0.165) and was superior to the 3-Item Stroke Scale (c-statistic: 0.73 versus 0.53, P<0.001) scores. Improvements in discrimination were also observed for the other outcomes. Conclusions: We derived a model that utilizes clinical text from paramedic reports to identify stroke. Our results require validation but have the potential of improving prehospital routing protocols.
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What OpenQuestion holds
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.