ArticleScientific reports2026
Machine learning for predicting functional outcomes in acute ischemic stroke: insights from a nationwide stroke registry.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Interpretable machine learning-based prediction of 90-day mortality after endovascular treatment in acute large vessel occlusion stroke.Frontiers in neurology · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Accurately predicting the prognosis of patients with acute ischemic stroke at discharge remains highly challenging after active treatment. The aim of this retrospective nationwide registry-based study was to identify key predictors associated with favorable outcomes and to develop machine learning models for patient outcome prediction. Analysis of a comprehensive dataset of 40,586 patients revealed younger age (odds ratio [OR]: 0.975; 95% confidence interval [CI]: 0.972-0.977; p < 0.001), lower initial National Institutes of Health Stroke Scale score (OR: 0.862; 95% CI: 0.855-0.868; p < 0.001), mechanical thrombectomy (OR: 2.617; 95% CI: 2.185-3.134; p < 0.001), and rehabilitation therapy (OR: 2.765; 95% CI: 2.530-3.022; p < 0.001) as significant predictors of good functional outcome. We developed three machine learning models-random forest (RF), support vector machine (SVM), and logistic regression-to predict favorable functional outcomes (modified Rankin Scale score, ≤ 2) at discharge. Among these, the RF model revealed superior predictive performance, achieving an area under the curve (AUC) of 0.87, compared to the SVM and logistic regression, each achieving an AUC of 0.80. This study underscores the transformative potential of machine learning in stroke management, predicting and improving patient outcomes and streamlining healthcare delivery.
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Registered trials
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