ArticleFrontiers in neuroscience2023
A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: A real-world retrospective study.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients.Acta neurologica Belgica · 2025Pooled it
- Artifical intelligence-powered delta-NIHSS-based model for predicting recurrence, disability and mortality after acute ischaemic strokes (DISCO): a modelling study.EClinicalMedicine · 2025Article
- Interpretable multiclassification machine learning models in predicting the extent of small cell lung cancer metastasis.Scientific reports · 2025Article
- Implementation and Updating of Clinical Prediction Models: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- Enhancing stroke risk prediction through class balancing and data augmentation with CBDA-ResNet50.Scientific reports · 2025Article
- Hyperhomocysteinemia-Driven Ischemic Stroke: Unraveling Molecular Mechanisms and Therapeutic Horizons.Food science & nutrition · 2025Review
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Knowledge map of artificial intelligence in neurodegenerative diseases: a decade-long bibliometric and visualization study.Frontiers in aging neuroscience · 2025Article
- Risk factors and prediction models for recurrent acute ischemic stroke: a retrospective analysis.PeerJ · 2024Article
- A clinical prediction model for distant metastases of pediatric neuroblastoma: an analysis based on the SEER database.Frontiers in pediatrics · 2024Article
- A machine learning-based model for predicting distant metastasis in patients with rectal cancer.Frontiers in oncology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: Recurrent stroke accounts for 25-30% of all preventable strokes, and this study was conducted to establish a machine learning-based clinical predictive rice idol for predicting stroke recurrence within 1 year in patients with acute ischemic stroke (AIS). Methods: A total of 645 AIS patients at The Second Affiliated Hospital of Xuzhou Medical University were screened, included and followed up for 1 year for comprehensive clinical data. Univariate and multivariate logistic regression (LR) were used to screen the risk factors of stroke recurrence. The data set was randomly divided into training set and test set according to the ratio of 7:3, and the following six prediction models were established by machine algorithm: random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM) and LR. The model with the strongest prediction performance was selected by 10-fold cross-validation and receiver operating characteristic (ROC) curves, and the models were investigated for interpretability by SHAP. Finally, the models were constructed to be visualized using a web calculator. Results: Logistic regression analysis showed that right hemisphere, homocysteine (HCY), C-reactive protein (CRP), and stroke severity (SS) were independent risk factors for the development of stroke recurrence in AIS patients. In 10-fold cross-validation, area under curve (AUC) ranked from 0.777 to 0.959. In ROC curve analysis, AUC ranged from 0.887 to 0.946. RF model has the best ability to predict stroke recurrence, and HCY has the largest contribution to the model. A web-based calculator https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/ has been developed accordingly. Conclusion: This study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance.
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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.