Predicting Early Dysphagia in Acute Ischemic Stroke Using an Explainable Machine Learning Model
ML models may serve as reliable assessment tools for predicting dysphagia in patients with AIS. The RF model demonstrated the best predictive performance. This predictive model could assist clinical healthcare providers in delivering early warnings and developing individualized treatment plans for high-risk patients.
Authors
- Bei Tian (ORCID: https://orcid.org/0009-0007-4497-5650)
- Jiayan Tang
- Su-Fang Yu
- Xiaojuan Yu
- Hongling Qu (ORCID: https://orcid.org/0000-0002-8876-6419)
- Ye Li (ORCID: https://orcid.org/0009-0008-5541-555X)
- Yongfang Zhang
Institutions
- Shanghai University of Traditional Chinese Medicine (CN)
- Longhua Hospital Shanghai University of Traditional Chinese Medicine (CN)
- Pudong Medical Center (CN)
- Shanghai University of Medicine and Health Sciences (CN)
Publication Details
- Journal
- International Journal of General Medicine
- Published
- 2025-12-01
- DOI
- https://doi.org/10.2147/ijgm.s567157
- Primary Topic
- Dysphagia Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shanghai Municipal Health Commission
- Shanghai Pudong New Area Health Commission