A machine learning predictive model for acute kidney injury among aneurysmal subarachnoid hemorrhage patients
The random forest model demonstrated superior performance in predicting AKI in aSAH patients, achieving a high AUC value, predictive accuracy, and remarkable stability. This model could help clinicians evaluate the risk of AKI in the early stage and guide therapeutic options among aSAH patients.
Authors
- Linrui Cai
- Yunhui Zeng (ORCID: https://orcid.org/0009-0002-3186-6854)
- Ruoran Wang (ORCID: https://orcid.org/0000-0001-5269-3863)
- Lei Qian (ORCID: https://orcid.org/0000-0002-0153-2717)
- Yu Zhang (ORCID: https://orcid.org/0000-0003-0978-2363)
- Min He (ORCID: https://orcid.org/0000-0001-8869-0146)
- Jianguo Xu (ORCID: https://orcid.org/0000-0001-9651-8186)
Institutions
- Sichuan University (CN)
- Wenzhou Medical University (CN)
- West China Hospital of Sichuan University (CN)
- Zhejiang Taizhou Hospital (CN)
- Taizhou University (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-11-10
- DOI
- https://doi.org/10.1186/s12911-025-03156-8
- Primary Topic
- Aortic aneurysm repair treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chinese Academy of Sciences
- Sichuan University
- West China Hospital, Sichuan University