Distinct immunological signatures define three sepsis recovery trajectories: a multi-cohort machine learning study
Early prediction of sepsis recovery trajectories is feasible and may facilitate personalized treatment strategies. The developed model could assist clinical decision-making and resource allocation in critical care settings.
Authors
- Jingyi Wu (ORCID: https://orcid.org/0000-0002-5311-4795)
- Rui Zhang (ORCID: https://orcid.org/0000-0003-1279-7937)
- Ruoming Tan (ORCID: https://orcid.org/0000-0002-4815-0695)
- Fang Long
Institutions
- Shanghai Jiao Tong University (CN)
- Ruijin Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-04-17
- DOI
- https://doi.org/10.3389/fmed.2025.1575237
- Citations
- 3
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.06