A machine learning model for prediction of cardiac arrest-associated acute kidney injury in the ICU: an internal and external validation study
An externally validated logistic regression model incorporating 10 variables effectively predicted early acute kidney injury onset in cardiac arrest patients. The SHapley Additive exPlanations algorithm facilitated model interpretation, helping clinicians understand the contribution of each variable to acute kidney injury risk, to determine which factors contribute most significantly to patient risk.
Authors
- Peifeng Ni (ORCID: https://orcid.org/0000-0002-2963-2648)
- C. Wu
- Shangli Cheng (ORCID: https://orcid.org/0000-0002-8501-086X)
- MengYuan Diao
- Wei Hu
- Wenbo Xu
- Gensheng Zhang
- Chen Li
- Tianhao Ni
Institutions
- Zhejiang Chinese Medical University (CN)
- Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-01-20
- DOI
- https://doi.org/10.3389/fmed.2025.1717973
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Zhejiang Chinese Medical University