Clinical validation and optimization of machine learning models for early prediction of sepsis
The RF model we developed showed the greatest potential for early prediction of sepsis in admitted patients, which could aid clinicians in their decision-making process. Our findings also suggested that male patients with bacterial infections and high procalcitonin levels, lower albumin levels, or prolonged prothrombin times were more likely to develop sepsis.
Authors
- Yuting Luo (ORCID: https://orcid.org/0000-0003-4502-8659)
- Jinyu Xia (ORCID: https://orcid.org/0000-0001-6583-5381)
- Fei Xiao (ORCID: https://orcid.org/0000-0002-9266-9443)
- Xu Liu (ORCID: https://orcid.org/0000-0002-1620-3932)
- Meiyi Li (ORCID: https://orcid.org/0000-0002-0178-7883)
- Dong Yang (ORCID: https://orcid.org/0000-0002-1717-6833)
- Hui Ouyang
- Jia He (ORCID: https://orcid.org/0009-0002-4766-6718)
- Xi Liu (ORCID: https://orcid.org/0000-0002-4785-2468)
Institutions
- Sun Yat-sen University (CN)
- Fifth Affiliated Hospital of Sun Yat-sen University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-02-05
- DOI
- https://doi.org/10.3389/fmed.2025.1521660
- Citations
- 3
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.06
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Basic and Applied Basic Research Foundation of Guangdong Province