A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning
The interpretable XGBoost-based prognostic model for SFTS shows high predictive accuracy and has been translated into a clinical tool. The model's 7 key features serve as valuable indicators for early prognosis of SFTS, warranting close attention from healthcare professionals in clinical practice.
Authors
- Yishan Zheng (ORCID: https://orcid.org/0009-0006-6701-8354)
- Jing Xie (ORCID: https://orcid.org/0000-0003-3278-2812)
- Jian-she Xu
- Kai Yang (ORCID: https://orcid.org/0000-0003-0760-1215)
- Bin Quan
Institutions
- Nanjing University of Chinese Medicine (CN)
- First Affiliated Hospital of Wannan Medical College (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Microbiology
- Published
- 2025-03-19
- DOI
- https://doi.org/10.3389/fmicb.2025.1557922
- Citations
- 3
- Primary Topic
- Viral Infections and Vectors
- Type
- article
- Field-Weighted Citation Impact
- 4.19