ArticleScientific reports2023
Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy.Frontiers in bioengineering and biotechnology · 2023Pooled it
- Prediction models for aspiration risk in stroke patients: a systematic review.Frontiers in neurologyPooled it
- 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 for dysphagia screening: A machine learning approach.PLOS digital health · 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
- Dynamic suprahyoid muscle ultrasound in acute stroke: a prospective observational study for developing a dysphagia severity score.European journal of physical and rehabilitation medicine · 2026Observational
- Interpretable machine learning for accessible dysphagia screening and staging in older adults.iScience · 2026Article
- Multi-Center Validation of Artificial Intelligence-Based Video Analysis Platform for Automatic Evaluation of Swallowing Disorders.Diagnostics (Basel, Switzerland) · 2025Article
- Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.Auris, nasus, larynx · 2025Review
- Predicting Long-Term Prognosis of Poststroke Dysphagia with Machine Learning.Journal of clinical medicine · 2025Article
- Article
- Comprehensive Analysis of the SUMO-related Signature: Implication for Diagnosis, Prognosis, and Immune Therapeutic Approaches in Cervical Cancer.Biochemical genetics · 2024Article
- Application of intelligent technologies for dysphagia risk prediction: A scoping review.Digital healthReview
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Dysphagia is a fatal condition after acute stroke. We established machine learning (ML) models for screening aspiration in patients with acute stroke. This retrospective study enrolled patients with acute stroke admitted to a cerebrovascular specialty hospital between January 2016 and June 2022. A videofluoroscopic swallowing study (VFSS) confirmed aspiration. We evaluated the Gugging Swallowing Screen (GUSS), an early assessment tool for dysphagia, in all patients and compared its predictive value with ML models. Following ML algorithms were applied: regularized logistic regressions (ridge, lasso, and elastic net), random forest, extreme gradient boosting, support vector machines, k-nearest neighbors, and naïve Bayes. We finally analyzed data from 3408 patients, and 448 of them had aspiration on VFSS. The GUSS showed an area under the receiver operating characteristics curve (AUROC) of 0.79 (0.77-0.81). The ridge regression model was the best model among all ML models, with an AUROC of 0.81 (0.76-0.86), an F1 measure of 0.45. Regularized logistic regression models exhibited higher sensitivity (0.66-0.72) than the GUSS (0.64). Feature importance analyses revealed that the modified Rankin scale was the most important feature of ML performance. The proposed ML prediction models are valid and practical for screening aspiration in patients with acute stroke.
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