Interpretable machine learning model for predicting delirium in patients with sepsis: a study based on the MIMIC data
The nomogram built on the XGBoost model provides clinicians with an easy tool to quickly assess the risk of developing delirium in patients with sepsis. It provides a new idea and direction for the best model to predict delirium in patients with sepsis, so as to promote the development of delirium related research.
Authors
- Jiangquan Yu (ORCID: https://orcid.org/0000-0001-9338-4311)
- Lulu Wang (ORCID: https://orcid.org/0000-0001-7466-9522)
- Xia Li (ORCID: https://orcid.org/0000-0003-3050-8529)
- Jing Fu
- Ruiqiang Zheng (ORCID: https://orcid.org/0009-0001-3774-9918)
- Aifeng He
Institutions
- Xuzhou Medical College (CN)
- Second People’s Hospital of Huai’an (CN)
- Yancheng Second People's Hospital (CN)
- Northern Jiangsu People's Hospital (CN)
- Yangzhou University (CN)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2025-04-23
- DOI
- https://doi.org/10.1186/s12879-025-10982-8
- Citations
- 3
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 4.64