ArticleJournal of neurointerventional surgery2023
An explainable machine learning model for predicting the outcome of ischemic stroke after mechanical thrombectomy.
Article in Journal of neurointerventional surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Hypertriglyceridemia predicts good neurological outcome after mechanical thrombectomy in patients with acute ischemic stroke due to large-vessel disease.Postepy w kardiologii interwencyjnej = Advances in interventional cardiology · 2026Article
- Prognostic Modeling Based on Post-Endovascular Thrombectomy Systolic Blood Pressure Trajectories Using Explainable Artificial Intelligence: A Secondary Analysis of the OPTIMAL-BP Trial.Journal of medical systems · 2026Observational
- Development and validation of an explainable machine learning prediction model for futile recanalization after mechanical thrombectomy in acute large vessel occlusion stroke.Journal of neurointerventional surgery · 2026Article
- Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding.Animal nutrition (Zhongguo xu mu shou yi xue hui) · 2025Article
- Identification and Patient Benefit Evaluation of Machine Learning Models for Predicting 90-Day Mortality After Endovascular Thrombectomy Based on Routinely Ready Clinical Information.Bioengineering (Basel, Switzerland) · 2025Article
- AI prediction model for endovascular treatment of vertebrobasilar occlusion with atrial fibrillation.NPJ digital medicine · 2025Article
- AI-driven integration of imaging and radiology language improves stroke mortality prediction.Frontiers in neurology · 2025Article
- Development and validation of a clinical model for predicting 90-day outcomes after endovascular therapy with adjunctive tirofiban in acute ischemic stroke.Frontiers in neurology · 2025Article
- Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.Frontiers in neurology · 2025Article
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- 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
- Interpretable machine learning model predicting immune checkpoint inhibitor-induced hypothyroidism: A retrospective cohort study.Cancer science · 2024Article
- Construction of a machine learning-based prediction model for unfavorable discharge outcomes in patients with ischemic stroke.Heliyon · 2024Article
- Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis.Heliyon · 2024Article
- Comprehensive predictive model for cerebral microbleeds: integrating clinical and biochemical markers.Frontiers in neuroscience · 2024Article
- Interpretable Machine Learning Model Predicting Early Neurological Deterioration in Ischemic Stroke Patients Treated with Mechanical Thrombectomy: A Retrospective Study.Brain sciences · 2023Article
- Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure.Frontiers in cardiovascular medicine · 2023Article
- AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke.Frontiers in neurology · 2023Article
- Risk stratification for stroke in acute persistent vertigo: development and internal validation of a multivariable prediction model.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is high variability in the clinical outcomes of patients with acute ischemic stroke (AIS) after mechanical thrombectomy (MT).
methods217 consecutive patients with anterior circulation large vessel occlusion who underwent MT between August 2018 and January 2022 were analysed. The primary outcome was functional independence defined as a modified Rankin Scale score of 0-2 at 3 months. In the derivation cohort (August 2018 to December 2020), 7 ensemble ML models were trained on 70% of patients and tested on the remaining 30%. The model's performance was further validated on the temporal validation cohort (January 2021 to January 2022). The SHapley Additive exPlanations (SHAP) framework was applied to interpret the prediction model.
resultsDerivation analyses generated a 9-item score (PFCML-MT) comprising age, National Institutes of Health Stroke Scale score, collateral status, and postoperative laboratory indices (albumin-to-globulin ratio, estimated glomerular filtration rate, blood neutrophil count, C-reactive protein, albumin and serum glucose levels). The area under the curve was 0.87 for the test set and 0.84 for the temporal validation cohort. SHAP analysis further determined the thresholds for the top continuous features. This model has been translated into an online calculator that is freely available to the public (https://zhelvyao-123-60-sial5s.streamlitapp.com).
conclusionsUsing ML and readily available features, we developed an ML model that can potentially be used in clinical practice to generate real-time, accurate predictions of the outcome of patients with AIS treated with MT.
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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.