Evaluating Predictive Performance of Machine Learning Algorithms That Integrate Routine Clinical Variables With Imaging-Derived Information in Stroke Recurrence Risk
ML-based algorithms that integrate routine clinical variables with imaging-derived data can predict stroke recurrence risk effectively, with the XGBoost model demonstrating superior predictive performance, which may further support more individualized clinical decision-making.
Authors
- Shitao Wang
- Li Gao (ORCID: https://orcid.org/0009-0002-3371-5013)
- Mingkun Zhang (ORCID: https://orcid.org/0000-0001-5321-0720)
- Jinlian Li
Institutions
- Shandong University of Traditional Chinese Medicine (CN)
- Fourth People’s Hospital of Jinan (CN)
- Shandong Provincial QianFoShan Hospital (CN)
- Jinan City People's Hospital (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- British Journal of Hospital Medicine
- Published
- 2026-01-26
- DOI
- https://doi.org/10.31083/bjhm50394
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00