Explainable machine learning models for early prediction of acute kidney injury after cardiac surgery
ML models, particularly the ensemble approach, provide reliable prediction of CSA-AKI, with generalizability validated in an external cohort. These models facilitate early identification of high-risk patients, enabling timely perioperative interventions to improve clinical outcomes.
Authors
- Changchun Cao (ORCID: https://orcid.org/0000-0003-1992-4257)
- Xiubin Liang (ORCID: https://orcid.org/0000-0001-9169-0390)
- Xin Hua Wan (ORCID: https://orcid.org/0000-0001-5226-4444)
- Qing Sun
- Mengqing Ma (ORCID: https://orcid.org/0000-0003-4402-7988)
- Xia Du (ORCID: https://orcid.org/0009-0005-9090-6264)
Institutions
- Nanjing Medical University (CN)
Publication Details
- Journal
- International Urology and Nephrology
- Published
- 2025-10-13
- DOI
- https://doi.org/10.1007/s11255-025-04807-5
- Citations
- 6
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 8.58
Funders
- National Natural Science Foundation of China