Machine learning models for predicting extended length of stay and hospital charges in nontraumatic subarachnoid hemorrhage
We constructed and validated ML models to predict extended LOS and hospital charges in SAH patients. The CatBoost model demonstrated strong predictive accuracy, while the decision tree model provided valuable insights into cost implications. Future multicenter studies are recommended to validate these models across diverse healthcare settings.
Authors
- Di Wu (ORCID: https://orcid.org/0000-0002-5622-7488)
- Yuxuan Wang (ORCID: https://orcid.org/0000-0002-1649-6974)
- Cong Wang (ORCID: https://orcid.org/0000-0001-9383-1732)
- Zhen Wang (ORCID: https://orcid.org/0000-0002-6377-3890)
- Sihan Wang (ORCID: https://orcid.org/0000-0002-3481-9167)
- Lingyu Hao
- Xingye Zhai
Institutions
- Tianjin Medical University General Hospital (CN)
- Qinghai University Affiliated Hospital (CN)
- Tianjin Infectious Diseases Hospital (CN)
- Ministry of Education (ME)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2026-02-04
- DOI
- https://doi.org/10.3389/fneur.2026.1737503
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00