ArticleFrontiers in neurology2022
Predicting 30-Day Readmission for Stroke Using Machine Learning Algorithms: A Prospective Cohort Study.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Factors associated with post-stroke readmission: a systematic review and meta analysis.Scientific reports · 2025Pooled it
- Rate and key predictors of unplanned readmission following ischemic stroke: a systematic review and meta-analysis.Frontiers in neurologyPooled it
- Prediction Models for Post-Stroke Hospital Readmission: A Systematic Review.Public health nursing (Boston, Mass.)Pooled it
- Predicting 30-Day Readmission After Stroke: A Systematic Review and Meta-Analysis to Inform Predictor Selection.Diagnostics (Basel, Switzerland) · 2026Review
- Proteomic Analysis of Differentially Expressed Proteins in Ischemic Stroke Recovery and Their Implications for Therapeutic Outcomes.Translational stroke research · 2026Article
- Machine learning-based survival models for predicting rehospitalization of older hip fracture patients: a retrospective cohort study.BMC musculoskeletal disorders · 2025Article
- Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke Within the UK Biobank.Healthcare (Basel, Switzerland) · 2025Article
- An exploration on the machine-learning-based stroke prediction model.Frontiers in neurology · 2024Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Machine learning algorithms for predicting 30-day stroke readmission are rarely discussed. The aims of this study were to identify significant predictors of 30-day readmission after stroke and to compare prediction accuracy and area under the receiver operating characteristic (AUROC) curve in five models: artificial neural network (ANN), K nearest neighbor (KNN), random forest (RF), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models. Methods: The subjects of this prospective cohort study were 1,476 patients with a history of admission for stroke to one of six hospitals between March, 2014, and September, 2019. A training dataset ( Results: For predicting 30-day readmission after stroke, the ANN model had significantly ( Conclusion: Using a machine-learning ANN model to obtain an accurate estimate of 30-day readmission for stroke and to identify risk factors may improve the precision and efficacy of management for these patients. For stroke patients who are candidates for PAC rehabilitation, these predictors have practical applications in educating patients in the expected course of recovery and health outcomes.
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