Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database
The XGBoost model effectively predicts SAE in elderly ICU patients and may serve as a reliable tool for clinicians to identify high-risk patients.
Authors
- Xiaodan Wu (ORCID: https://orcid.org/0000-0003-4602-502X)
- Yupeng Han (ORCID: https://orcid.org/0009-0005-9905-5080)
- Wangyu Li (ORCID: https://orcid.org/0000-0003-2436-053X)
- Xiyuan Xie
- Yijie Tang
- Jiapeng Qiu (ORCID: https://orcid.org/0009-0001-0192-6652)
- Zhiwei Song
Institutions
- Fujian Medical University (CN)
- First Affiliated Hospital of Fujian Medical University (CN)
- Fujian Provincial Hospital (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- Frontiers in Cellular and Infection Microbiology
- Published
- 2025-04-17
- DOI
- https://doi.org/10.3389/fcimb.2025.1545979
- Citations
- 15
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 20.31
Funders
- National Natural Science Foundation of China