ArticleInternational journal of general medicine2024
A Machine-Learning Model Based on Clinical Features for the Prediction of Severe Dysphagia After Ischemic Stroke.
Article in International journal of general medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Development and Validation of a Nomogram Model for Predicting the Severity of Post-Stroke Dysphagia Based on fNIRS-Derived Brain Functional Connectivity Features.Brain and behavior · 2026Article
- Machine learning-based screening tool for predicting the risk of oropharyngeal dysphagia in patients with ischemic stroke.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Artificial intelligence tools for the assessment and management of dysphagia: a scoping review.BMJ open · 2026Article
- Machine learning for dysphagia risk prediction in older adults after acute ischemic stroke.BMC medical informatics and decision making · 2026Article
- Machine learning-based model for prediction of swallowing recovery in post-stroke patients.BMC medical informatics and decision making · 2026Article
- Development and Validation of an Explainable Machine Learning Model for Identification of Dysphagia in Patients with COPD.International journal of chronic obstructive pulmonary disease · 2026Article
- A severity-anchored, patient-reported outcome framework for clinical trials in post-stroke dysphagia.Frontiers in neurology · 2026Article
- A Machine Learning Pipeline for Automated Bolus Segmentation and Area Measurement in Swallowing Videofluoroscopy Images of an Infant Pig Model.Dysphagia · 2025Article
- Predicting Long-Term Prognosis of Poststroke Dysphagia with Machine Learning.Journal of clinical medicine · 2025Article
- Predicting Early Dysphagia in Acute Ischemic Stroke Using an Explainable Machine Learning Model.International journal of general medicine · 2025Article
- Application of intelligent technologies for dysphagia risk prediction: A scoping review.Digital healthReview
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Background: This study aimed to construct machine-learning models for prediction of severe dysphagia after ischemic stroke based on clinical features and identify significant clinical predictors. Methods: Patients hospitalized with dysphagia after ischemic stroke in Affiliated Hospital of Jiangnan University were retrospectively analyzed and randomly divided into training and validation sets at a ratio of 7:3. Additional patients from Huai'an Hospital were selected as test set. 19 relevant clinical characteristics were collected. According to the water swallowing test (WST), patients were divided into severe dysphagia group and non-severe dysphagia group. K-nearest neighbor (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost) were applied to predict severe dysphagia. Receiver operating characteristic (ROC) curves were plotted, the area under the ROC (AUC) was calculated to assess predictive power, and DeLong's test was used to compare the AUCs among six models. Finally, an optimal model was obtained, and significant clinical predictors of severe dysphagia after stroke were screened. Results: A total of 724 patients were enrolled, 422 in training set, 182 in validation set and 120 in test set, respectively, with no statistically differences in baseline information ( Conclusion: Among all clinical feature-based machine-learning models for the prediction of severe dysphagia after ischemic stroke, XGBoost had the best predictive value.
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