ArticleDiagnostics (Basel, Switzerland)2023
XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Effects of oral care combined with neuromuscular electrical stimulation on clinical outcomes in the acute phase of acute ischemic stroke: a pilot randomized controlled trial.Journal of neuroengineering and rehabilitation · 2025Trial
- A Comparison of Machine Learning Algorithms for Predicting Hypertension Incidence Based on Cohort Study.Endocrinology, diabetes & metabolism · 2026Article
- Development and validation of an interpretable machine learning model integrating baseline multimodal CT perfusion and clinical data for predicting 9-month functional outcomes in acute ischemic stroke.Frontiers in neurology · 2026Article
- Constructing a predictive model for acute mastitis in lactating women based on machine learning.Scientific reports · 2025Article
- Machine Learning in Myasthenia Gravis: A Systematic Review of Prognostic Models and AI-Assisted Clinical Assessments.Diagnostics (Basel, Switzerland) · 2025Review
- Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP.BMC medical informatics and decision making · 2025Article
- Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics.BMC medical imaging · 2025Article
- Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.BMC medical informatics and decision making · 2025Article
- Personalized Predictions of Therapeutic Hypothermia Outcomes in Cardiac Arrest Patients with Shockable Rhythms Using Explainable Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning.Frontiers in pharmacology · 2025Article
- Article
- Predicting 3-month poor functional outcomes of acute ischemic stroke in young patients using machine learning.European journal of medical research · 2024Article
- Article
- Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke.Frontiers in neurology · 2024Article
- Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke.Scientific reports · 2023Article
- Explainable XGBoost model and nomogram for risk factor identification and risk prediction in cerebral small vessel disease: a machine learning-based retrospective cohort study.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
An all-inclusive and accurate prediction of outcomes for patients with acute ischemic stroke (AIS) is crucial for clinical decision-making. This study developed extreme gradient boosting (XGBoost)-based models using three simple factors-age, fasting glucose, and National Institutes of Health Stroke Scale (NIHSS) scores-to predict the three-month functional outcomes after AIS. We retrieved the medical records of 1848 patients diagnosed with AIS and managed at a single medical center between 2016 and 2020. We developed and validated the predictions and ranked the importance of each variable. The XGBoost model achieved notable performance, with an area under the curve of 0.8595. As predicted by the model, the patients with initial NIHSS score > 5, aged over 64 years, and fasting blood glucose > 86 mg/dL were associated with unfavorable prognoses. For patients receiving endovascular therapy, fasting glucose was the most important predictor. The NIHSS score at admission was the most significant predictor for those who received other treatments. Our proposed XGBoost model showed a reliable predictive power of AIS outcomes using readily available and simple predictors and also demonstrated the validity of the model for application in patients receiving different AIS treatments, providing clinical evidence for future optimization of AIS treatment strategies.
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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 Socratic graph.