ArticleTherapeutic advances in neurological disorders2025
Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods.
Article in Therapeutic advances in neurological disorders, 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.
- Predicting post-stroke functional outcome using explainable machine learning and integrated data.Scientific reports · 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
- Predicting intravenous thrombolysis outcomes in acute ischemic stroke using machine learning.Frontiers in neurologyArticle
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Authors and funding
25 authors.
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Abstract
Background: Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide. Intravenous thrombolysis (IVT) improves recovery, but predicting outcomes remains challenging. Machine learning (ML) and biomarkers like ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), S100 calcium-binding protein β (S100β), and neuron-specific enolase (NSE) may enhance prognostic accuracy. Objectives: We aimed to assess the predictive value of serum brain injury biomarkers for 3-month outcomes in AIS patients treated with IVT, using an ML-based model. Design: A multicenter prospective cohort study was conducted, enrolling AIS patients treated with recombinant tissue plasminogen activator from 16 hospitals. Methods: Of 1580 patients, 1028 were included and divided into training ( Results: The light gradient boosting machines (LightGBM) model achieved the best performance in the training dataset (AUC: 0.846; Conclusion: Integrating serum biomarkers (UCH-L1, S100β, NSE) with ML significantly improves 3-month outcome prediction in AIS patients. The LightGBM model offers robust performance and clinical interpretability for individualized treatment planning.
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