Predicting outcomes in pediatric patients with acute kidney injury: a retrospective single-center cohort study using machine learning models
Machine learning, particularly CatBoost, combined with survival analysis, accurately predicts AKI-related mortality in critically ill children, with lactate as a pivotal marker. These findings support precision risk stratification and early lactate-targeted interventions, though multicenter validation is needed for clinical adoption.
Authors
- Hong‐Han Ge (ORCID: https://orcid.org/0009-0004-0368-4599)
- Xusheng Jiang (ORCID: https://orcid.org/0000-0002-7370-0492)
- Y. Xu (ORCID: https://orcid.org/0000-0002-0763-9953)
- Feifei Shen
- Yan-Qun Sun
- Xu Wang (ORCID: https://orcid.org/0000-0003-4277-041X)
- Linjie Yu
Institutions
- Nantong University (CN)
- Affiliated Hospital of Nantong University (CN)
- Nanjing Children's Hospital (CN)
- Hangzhou Center for Disease Control and Prevention (CN)
- Children's Hospital of Zhejiang University (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
- Shandong First Medical University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-10-10
- DOI
- https://doi.org/10.1186/s12911-025-03211-4
- Citations
- 1
- Primary Topic
- Acute Kidney Injury Research
- Type
- article
- Field-Weighted Citation Impact
- 1.43