Development and validation of web-based, interpretable predictive models for sepsis and mortality in extensive burns
Machine learning models, RF and GBT, demonstrate strong predictive ability for sepsis and mortality in extensive burn patients. The application of SHAP enhances model transparency, facilitating clinical interpretation and early intervention. Two web-based calculators can guide intensive care strategies and improve patient outcomes.
Authors
- Si‐Ming Yuan (ORCID: https://orcid.org/0000-0003-1380-5425)
- Bin Zhou (ORCID: https://orcid.org/0000-0002-7206-6418)
- Mingyu Li (ORCID: https://orcid.org/0000-0003-4812-8604)
- Shiqi Wang (ORCID: https://orcid.org/0000-0003-2010-0132)
- Yong Chen (ORCID: https://orcid.org/0000-0002-7188-4566)
- Qiming Zheng (ORCID: https://orcid.org/0000-0002-7824-4262)
- Hai‐Yan Zhong
- Kan Qiu
Institutions
- Peking University (CN)
- Anqing City Hospital (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
- Jinling Institute of Technology (CN)
- Southeast University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Cellular and Infection Microbiology
- Published
- 2025-08-18
- DOI
- https://doi.org/10.3389/fcimb.2025.1586087
- Primary Topic
- Burn Injury Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00