ArticleFrontiers in neurology2025
Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multi-dimensional MRI representation and privileged learning approaches to functional outcome prediction for ischemic stroke patients.NPJ digital medicine · 2026Article
- Endovascular treatment and clinical outcomes in minor ischemic stroke due to large-vessel disease: a retrospective study.Frontiers in neuroscience · 2026Article
- Development and validation of machine learning models for predicting functional outcome after low-dose alteplase in the extended time window for acute ischemic stroke.Frontiers in neuroscience · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Acute ischemic stroke (AIS) patients often experience poor functional outcomes post-intravenous thrombolysis (IVT). Novel computational methods leveraging machine learning (ML) architectures increasingly support medical decision-making. We aimed to develop and validate a machine learning model to predict 3-month unfavorable functional outcome after IVT in AIS patients. Methods: This retrospective study developed ML prognostic models for 3-month functional outcome (modified Rankin scale scores of 3-6) in IVT-treated AIS patients. A derivation cohort ( Results: 938 patients constituted the derivation cohort (training: Conclusion: A parsimonious 5-variable LR model robustly predicts 3-month post-IVT outcomes, combining clinical utility, interpretability, and generalizability. NLR-driven inflammation is critical to prognosis. This tool facilitates early high-risk patient identification for personalized intervention.
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