Stage prediction of acute kidney injury in sepsis patients using explainable machine learning approaches
The machine learning models developed in this study effectively predicted the stages of AKI in severely ill sepsis patients, with the Random Forest model demonstrating optimal performance. SHAP analysis offered crucial insights into the risk factors, facilitating timely and personalized interventions within a clinical setting. Additional multi-center research is essential to confirm the validity of these findings and to ultimately improve patient outcomes and quality of life.
Authors
- Zhen Quan (ORCID: https://orcid.org/0000-0001-7782-3193)
- Siyao Zeng (ORCID: https://orcid.org/0000-0003-3179-2728)
- Jingkai Wang (ORCID: https://orcid.org/0000-0003-3604-9263)
- Yue Li
- Hongliang Wang (ORCID: https://orcid.org/0000-0001-6064-2820)
- Zheng Han (ORCID: https://orcid.org/0000-0002-2584-5187)
- Lianghe Wen
Institutions
- Harbin Medical University (CN)
- Second Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-10-15
- DOI
- https://doi.org/10.3389/fmed.2025.1667488
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Harbin Medical University
- National Natural Science Foundation of China
- National Key Research and Development Program of China