Machine Learning Based Prediction of Postoperative Acute Kidney Injury Risk in Coronary Artery Bypass Grafting Patients
The machine learning models constructed in this study can effectively predict post-CABG AKI, facilitating early identification of high-risk patients.
Authors
- Qingjie Wang (ORCID: https://orcid.org/0000-0002-7944-8128)
- Shoujie Feng
- Zhang Hu
- Ling Sun (ORCID: https://orcid.org/0000-0001-6444-9470)
- Dabei Cai (ORCID: https://orcid.org/0000-0001-6898-0664)
- Yang Zhang (ORCID: https://orcid.org/0000-0001-9290-5891)
- Zhihan Zhang (ORCID: https://orcid.org/0000-0003-0231-5033)
- Ye Deng
- Jun Wei (ORCID: https://orcid.org/0000-0001-9950-0467)
- Zhu Wang
Institutions
- Xuzhou Medical College (CN)
- Nanjing Traditional Chinese Medicine Hospital (CN)
- Changzhou No.2 People's Hospital (CN)
- Changzhou Third People's Hospital (CN)
- Wuxi People's Hospital (CN)
Publication Details
- Journal
- Clinical Interventions in Aging
- Published
- 2025-11-01
- DOI
- https://doi.org/10.2147/cia.s543303
- Citations
- 3
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 4.29
Funders
- Jiangsu Commission of Health
- Government of Jiangsu Province
- Nanjing Medical University