Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study
This study developed and validated a dynamic, stratified prediction system that provides stage-specific risk assessment for moderate-to-severe SA-AKI. The system underwent rigorous multiregional, multicenter validation and was translated into an interpretable clinical decision support tool, providing a scientific foundation for precision management.
Authors
- J F Zhang
- Hung Tai (ORCID: https://orcid.org/0009-0007-4863-5186)
- Wei Liu (ORCID: https://orcid.org/0000-0002-2688-0427)
- Shih-Fan Chen
- Huirui Han (ORCID: https://orcid.org/0000-0002-6932-3209)
- Jianshan Shi
- Biao Wang (ORCID: https://orcid.org/0000-0002-4358-7601)
- Weiwei Chen (ORCID: https://orcid.org/0000-0003-0218-2023)
- X. Ge (ORCID: https://orcid.org/0009-0009-9956-2532)
- Ying Zhang (ORCID: https://orcid.org/0009-0005-2668-0467)
Institutions
- First People's Hospital of Nanning (CN)
- Hainan Medical University (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-09-30
- DOI
- https://doi.org/10.2196/73840
- Citations
- 1
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35