Construction and evaluation of prediction model for renal function recovery in acute kidney injury patients undergoing continuous renal replacement therapy based on machine learning algorithms
<b>Conclusions:</b> Among the ML models developed using data from the two datasets to predict renal function recovery in critically ill AKI patients undergoing CRRT, the Lasso-LR model demonstrated the best performance. It can serve as a valuable tool for more efficient clinical disease management and prognostic evaluation.
Authors
- Liang Xu (ORCID: https://orcid.org/0000-0002-9039-6621)
- Xiaowei Ji (ORCID: https://orcid.org/0009-0000-7764-6844)
- Bo Xie (ORCID: https://orcid.org/0000-0003-0458-4202)
- Lei Zhong (ORCID: https://orcid.org/0000-0002-0208-1823)
- Xianghong Yang (ORCID: https://orcid.org/0000-0003-1685-4505)
- Y. T. Liang (ORCID: https://orcid.org/0009-0000-7282-9481)
Institutions
- Zhejiang Chinese Medical University (CN)
- Huzhou Normal University (CN)
- Huzhou Central Hospital (CN)
- Digital Science (United States) (US)
- Baim Institute for Clinical Research (US)
- Zhejiang Provincial People's Hospital (CN)
- Hangzhou Medical College (CN)
Publication Details
- Journal
- Annals of Medicine
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1080/07853890.2025.2561794
- Citations
- 1
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 1.43