Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database
The stack ensemble model developed by us achieved AUC values of 0.853 and 0.802 in internal and external validation cohorts respectively and also demonstrated excellent performance in other metrics. It serves as a reliable tool for predicting AKI in patients with acute pancreatitis complicated by sepsis.
Authors
- Zhu Wang (ORCID: https://orcid.org/0000-0002-7821-8574)
- ZhiRong Xue (ORCID: https://orcid.org/0009-0007-9001-3807)
- Zhan Wang (ORCID: https://orcid.org/0000-0002-4225-8136)
- Ying Zhou (ORCID: https://orcid.org/0009-0005-5439-2235)
- Junjie Cai (ORCID: https://orcid.org/0000-0001-7508-2176)
- FuYuan Li
- Ruixiang Bian
Institutions
- Qinghai University (CN)
- Qinghai University Affiliated Hospital (CN)
Publication Details
- Journal
- BMJ Open
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1136/bmjopen-2024-087427
- Citations
- 3
- Primary Topic
- Pancreatitis Pathology and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.02
Funders
- National Natural Science Foundation of China