Machine learning-based prediction of short-term outcomes in aneurysmal subarachnoid hemorrhage: a multicenter study integrating clinical and inflammatory indicators
The GBM-based model enables early prediction of poor short-term outcomes in aSAH, supporting timely clinical decision-making. Prospective multicenter validation is warranted to confirm its generalizability across diverse populations.
Authors
- Yue Xiao (ORCID: https://orcid.org/0000-0002-0963-2907)
- Xiaoxing Liao (ORCID: https://orcid.org/0000-0001-7198-2505)
- Hui Shi (ORCID: https://orcid.org/0000-0002-0104-8212)
- Wei Zhou (ORCID: https://orcid.org/0000-0002-3155-1230)
- Yuelin Li (ORCID: https://orcid.org/0000-0001-6571-3587)
- Mingfeng Wang (ORCID: https://orcid.org/0000-0002-6445-6854)
- Su Hai
- Cong Peng (ORCID: https://orcid.org/0000-0002-7447-7384)
- Peize Li
- Zhuo Wang (ORCID: https://orcid.org/0009-0006-3418-3118)
Institutions
- Central South University (CN)
- Shanxi Medical University (CN)
- Chinese PLA General Hospital (CN)
- First Hospital of Shanxi Medical University (CN)
- Second Hospital of Shanxi Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- BMC Medicine
- Published
- 2025-11-29
- DOI
- https://doi.org/10.1186/s12916-025-04523-y
- Citations
- 2
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 2.78