Observational studyScientific reports2021
A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study.
Observational study in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review and Meta-Analysis of Prehospital Machine Learning Scores as Screening Tools for Early Detection of Large Vessel Occlusion in Patients With Suspected Stroke.Journal of the American Heart Association · 2024Pooled it
- Machine learning models powered by emergency medical services data enhance stroke triage in prehospital settings.Scientific reports · 2026Article
- The Role of Artificial Intelligence in Enhancing Diagnostic and Triage Accuracy in Hospital Emergency Departments: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2026Review
- Large vessel occlusion prediction scale thresholds that are sensitive for DAWN Trial patients.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- The effect of social alienation on stigma among stroke inpatients in China: a cross-sectional study.BMC public health · 2025Article
- Anston attentional network for structured data based stroke risk prediction in smart aging.Scientific reports · 2025Article
- Review
- Emergency medical services providers' perspectives on the use of artificial intelligence in prehospital identification of stroke- a qualitative study in Norway and Sweden.BMC emergency medicine · 2025Article
- Systematic Review of Prehospital Prediction Models for Identifying Intracerebral Haemorrhage in Suspected Stroke Patients.Healthcare (Basel, Switzerland) · 2025Review
- Large-Vessel Occlusion Stroke Knowledge and Training for Stroke Severity Assessment Among Emergency Medical Services Personnel in the United States.Stroke (Hoboken, N.J.) · 2025Article
- Early stroke detection through machine learning in the prehospital setting.Frontiers in cardiovascular medicine · 2025Article
- Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.Prehospital and disaster medicine · 2024Article
- Article
- Development and internal validation of prehospital prediction models for identifying intracerebral haemorrhage in suspected stroke patients.BMJ neurology open · 2024Article
- The Capacitated Maximal Covering Location Problem Improves Access to Stroke Treatment: A Cross-Sectional Simulation Study.Health services insights · 2024Article
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value. Consecutive adult patients with suspected stroke as per emergency medical service personnel were enrolled in a prospective multicenter observational study in 12 hospitals in Japan. Five diagnostic algorithms using machine learning, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting, were evaluated for stroke and subcategories including acute ischemic stroke with/without large vessel occlusions, intracranial hemorrhage, and subarachnoid hemorrhage. Of the 1446 patients in the analysis, 1156 (80%) were randomly included in the training (derivation) cohort and cohorts, and 290 (20%) were included in the test (validation) cohort. In the diagnostic algorithms for strokes using eXtreme Gradient Boosting had the highest diagnostic value (test data, area under the receiver operating curve 0.980). In the diagnostic algorithms for the subcategories using eXtreme Gradient Boosting had a high predictive value (test data, area under the receiver operating curve, acute ischemic stroke with/without large vessel occlusions 0.898/0.882, intracranial hemorrhage 0.866, subarachnoid hemorrhage 0.926). Prehospital diagnostic algorithms using machine learning had high predictive value for strokes and their subcategories.
Indexed as
Identifiers
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.