Construction of a risk prediction model for pulmonary infection in patients with spontaneous intracerebral hemorrhage during the recovery phase based on machine learning
This study provides a high-performing and interpretable ML-based risk stratification tool for pulmonary infection in patients during the recovery phase of deep SICH. The integration of SHAP enhances clinical applicability by demystifying complex model outputs, thereby supporting individualized preventive strategies. These findings underscore the promise of explainable AI in advancing neurocritical care and call for prospective multicenter validation and real-time dynamic model adaptation in future research.
Authors
- Feng Zhu (ORCID: https://orcid.org/0000-0002-9356-2154)
- Liang Chen (ORCID: https://orcid.org/0000-0001-6644-477X)
- Xiaomei Zhou (ORCID: https://orcid.org/0000-0002-2696-2000)
- Xiaoxiao Han
- Jixiang Xu (ORCID: https://orcid.org/0000-0001-6942-7588)
- Yuan Li (ORCID: https://orcid.org/0000-0001-9312-0885)
- Yinliang Qi (ORCID: https://orcid.org/0000-0002-4270-4087)
Institutions
- Wannan Medical College (CN)
- Anhui Medical University (CN)
- Second Affiliated Hospital of Anhui Medical University (CN)
- Dazhou Central Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-06-18
- DOI
- https://doi.org/10.3389/fneur.2025.1571755
- Citations
- 1
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Anhui Medical University