An interpretable machine learning model for predicting sepsis risk in ICU patients with non-traumatic subarachnoid hemorrhage: development and validation using the MIMIC-IV database
The machine learning model developed based on the MIMIC-IV database can effectively predict the risk of sepsis in ICU patients with non-traumatic SAH. It demonstrates good interpretability and clinical utility, providing a basis for clinical risk stratification and precise intervention.
Authors
- Bingsha Han
- G Li
- Jing Xia (ORCID: https://orcid.org/0000-0002-4496-5520)
- Yu Chen (ORCID: https://orcid.org/0000-0002-9457-6195)
- Shaojie Guo (ORCID: https://orcid.org/0009-0000-0231-0182)
- Jv Wang
- Xinchen Ma
- Ang Li
- Guang Feng
- Yang Liu
Institutions
- Henan Provincial People's Hospital (CN)
- Henan Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2026-01-21
- DOI
- https://doi.org/10.3389/fneur.2026.1734264
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Henan Provincial People's Hospital