A machine learning-based predictive nomogram for early neurological improvement after thrombolysis in acute ischemic stroke
This ML-based model effectively predicts ENI in rt-PA-treated AIS patients by integrating critical clinical and biochemical markers. Its application may optimize personalized treatment strategies, enhance clinical decision-making, and improve patient outcomes.
Authors
- Ruiting Hu
- Ningqin Meng (ORCID: https://orcid.org/0000-0003-1966-9815)
- Qin Zhu
- Chao Qin (ORCID: https://orcid.org/0000-0003-4727-5055)
- Bing-Hua Lv
- Hao-wei Deng
- Guiming Weng
Institutions
- Guangxi Medical University (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-11-12
- DOI
- https://doi.org/10.3389/fneur.2025.1662498
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangxi Province