Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis
A parsimonious 5-variable LR model robustly predicts 3-month post-IVT outcomes, combining clinical utility, interpretability, and generalizability. NLR-driven inflammation is critical to prognosis. This tool facilitates early high-risk patient identification for personalized intervention.
Authors
- Wei Zhang (ORCID: https://orcid.org/0000-0003-0192-5956)
- Xin Yang (ORCID: https://orcid.org/0000-0003-3428-3806)
- Runlu Cai (ORCID: https://orcid.org/0009-0003-1307-9563)
- Guiyun Cui (ORCID: https://orcid.org/0000-0003-0251-0648)
- F. Bu (ORCID: https://orcid.org/0009-0008-4857-0068)
- Xiaohong Tang (ORCID: https://orcid.org/0000-0002-2666-4548)
Institutions
- Xuzhou Medical College (CN)
- Huaian First People’s Hospital (CN)
- First Affiliated Hospital of Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-09-26
- DOI
- https://doi.org/10.3389/fneur.2025.1668816
- Citations
- 1
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Natural Science Foundation of China
- Government of Jiangsu Province