Early mortality risk prediction in severe fever with thrombocytopenia syndrome using an interpretable machine learning model based on routine clinical parameters
We developed and validated a high-performance, interpretable ML model for SFTS prognosis relying on only six readily available parameters. By deploying this parsimonious model as an online calculator, we provide a practical decision-support tool to facilitate early risk stratification and timely intervention, particularly in resource-limited settings.
Authors
- Qian Dai
- Huichao Chen (ORCID: https://orcid.org/0000-0002-3012-6531)
- Qiong Lu (ORCID: https://orcid.org/0000-0002-0126-4978)
- Yuanyuan Hu (ORCID: https://orcid.org/0000-0001-8511-1401)
- Liangfei Xu
- Tong Shao Tong
- Ji Guo
- Ying Wang
Institutions
- University of Science and Technology of China (CN)
- Anhui Medical University (CN)
- Chaohu Hospital of Anhui Medical University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2026-03-09
- DOI
- https://doi.org/10.3389/fpubh.2026.1776344
- Primary Topic
- Viral Infections and Vectors
- Type
- article
- Field-Weighted Citation Impact
- 0.00