ArticleScientific reports2021
Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- AKI-twinX: explainable organ structured digital twin for sepsis AKI trajectory forecasting.medRxiv : the preprint server for health sciences · 2026Article
- Development and Validation of a Web-Based Machine Learning Model for Predicting Early Neurological Deterioration Following Stroke Thrombolysis: Multicenter Study.Journal of medical Internet research · 2025Article
- Anston attentional network for structured data based stroke risk prediction in smart aging.Scientific reports · 2025Article
- Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy.Journal of clinical medicine · 2025Review
- Abducens nerve palsy in patients with spontaneous intraventricular hemorrhage: A case report.Medicine · 2025Article
- Machine learning and deep learning predictive models for prognosis in patients with atrial fibrillation: a systematic review and meta-analysis.BMJ digital health & AI · 2025Article
- Predictive value of net water uptake for early neurological deterioration after mechanical thrombectomy in acute ischemic stroke with large vessel occlusion.Frontiers in neurology · 2025Article
- Machine learning models based on location-radiomics enable the accurate prediction of early neurological function deterioration for acute stroke in elderly patients.Frontiers in aging neuroscience · 2025Article
- Dynamic, Interpretable, Machine Learning-Based Outcome Prediction as a New Emerging Opportunity in Acute Ischemic Stroke Patient Care: A Proof-of-Concept Study.Stroke research and treatment · 2025Article
- Machine Learning Differentiation of Autism Spectrum Sub-Classifications.Journal of autism and developmental disorders · 2024Article
- Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Rescue therapy of early neurological deterioration in lacunar stroke.BMC neurology · 2024Article
- Artificial intelligence and atrial fibrillation: A bibliometric analysis from 2013 to 2023.Heliyon · 2024Review
- Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank.Heliyon · 2024Article
- Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study.Frontiers in neurology · 2024Article
- Machine learning determination of applied behavioral analysis treatment plan type.Brain informatics · 2023Article
- Explainable AI for clinical and remote health applications: a survey on tabular and time series data.Artificial intelligence review · 2023Article
- Predicting short-term outcomes in atrial-fibrillation-related stroke using machine learning.Frontiers in neurology · 2023Article
- Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy.Frontiers in neurology · 2023Article
- Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus-A Preliminary Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
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Authors and funding
22 authors.
Funding
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Abstract
We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715-0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.
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