Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study
The stacking ensemble model developed in this study yields accurate and robust predictions of SALI in patients with sepsis, demonstrating potential clinical utility for early intervention and personalized treatment strategies.
Authors
- Jingchao Lei (ORCID: https://orcid.org/0009-0005-0519-2173)
- Chuanzheng Sun (ORCID: https://orcid.org/0000-0003-2116-0233)
- Yao Zhang (ORCID: https://orcid.org/0000-0002-7481-4053)
- Jing Qi (ORCID: https://orcid.org/0000-0001-9558-6742)
- Jia Zhai
Institutions
- Central South University (CN)
- Third Xiangya Hospital (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-05-26
- DOI
- https://doi.org/10.2196/66733
- Citations
- 13
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 17.60