A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study
The XGBoost model using circulating immune cell parameters accurately predicted the risk of ARDS in patients with sepsis. This model could be a useful tool for the early identification of high-risk patients and timely intervention; however, further validation and integration into clinical practice are required.
Authors
- Qin Lian (ORCID: https://orcid.org/0000-0003-2028-2706)
- Qianqian Qin (ORCID: https://orcid.org/0000-0001-8015-5261)
- Junyu Lu (ORCID: https://orcid.org/0000-0001-7967-0813)
- Haiqing Gao (ORCID: https://orcid.org/0000-0003-2230-0576)
- Chenglin Mou
- Tao Qin (ORCID: https://orcid.org/0009-0006-2511-8373)
- Kaihuan Zhou (ORCID: https://orcid.org/0009-0002-6648-0337)
- Yin Chen
- Yicong Ling
Institutions
- Guangxi Medical University (CN)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1186/s12879-025-10974-8
- Citations
- 8
- Primary Topic
- Respiratory Support and Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 12.15
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangxi Province
- Guangxi Medical University
- Guangxi Key Research and Development Program