ArticleOxford open neuroscience2024
Machine learning-based prediction of one-year mortality in ischemic stroke patients.
Article in Oxford open neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- A Heterogeneous Graph Neural Network Framework for Multi-Horizon Stroke Mortality Prediction.medRxiv : the preprint server for health sciences · 2026Article
- Development and web-based implementation of a machine learning model for predicting one-year adverse outcomes in acute ischaemic stroke.Frontiers in medicine · 2026Article
- Artifical intelligence-powered delta-NIHSS-based model for predicting recurrence, disability and mortality after acute ischaemic strokes (DISCO): a modelling study.EClinicalMedicine · 2025Article
- A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction.Scientific reports · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic stroke.
methodsFive machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model's outcomes and defined the influential predictive factors.
resultsAnalyzing 8183 ischemic stroke patients, logistic regression achieved 83% accuracy, 0.89 AUC, and an F1 score of 0.83. Significant predictors included stroke severity, pre-stroke functional status, age, hospital-acquired pneumonia, ischemic stroke subtype, tobacco use, and co-existing diabetes mellitus (DM). DISCUSSION: The model highlights the importance of predicting mortality in enhancing personalized stroke care. Apart from pneumonia, all predictors can serve the early prediction of mortality risk which supports the initiation of early preventive measures and in setting realistic expectations of disease outcomes for all stakeholders. The identified tobacco paradox warrants further investigation.
conclusionThis study offers a promising tool for early prediction of stroke mortality and for advancing personalized stroke care. It emphasizes the need for prospective studies to validate these findings in diverse clinical settings.
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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.