An interpretable machine-learning model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury
This study established an externally validated and interpretable ML model for riskstratification in S-AKI, enabling early identification of high-risk patients, personalized management strategies, and enhanced clinical outcomes in sepsis care.
Authors
- Jianjun Wang (ORCID: https://orcid.org/0000-0001-7039-7136)
- Rongrong Ma (ORCID: https://orcid.org/0009-0002-8380-8170)
- Mei Yang
- Nan Zhang
- Wen-Jie Zhou
- Yao-Geng Chen
Institutions
- Ningxia Medical University (CN)
- Ningxia Medical University General Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-02-13
- DOI
- https://doi.org/10.3389/fmed.2026.1756831
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00