BOBM: an adaptive deep learning framework for extended-window sepsis prediction with cross-institutional generalizability
In contrast to traditional prediction approaches, our model extends the potential intervention timeframe significantly, while incorporating dynamic monitoring capabilities spanning the 7- to 28-day window before clinical suspicion, thereby establishing a foundation for regional sepsis surveillance systems.
Authors
- Ren Mo
- Haoyu Zheng
- Dan Shao (ORCID: https://orcid.org/0009-0001-6235-6067)
- Kai Xiao
- Boyuan Gu
- Wanxuan Li
- Qiang Wu
- Qiong Yang
Institutions
- Inner Mongolia University (CN)
- Inner Mongolia People's Hospital (CN)
- Hohhot First Hospital (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2026-01-15
- DOI
- https://doi.org/10.3389/fmed.2025.1731854
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Fundamental Research Funds for the Central Universities