Multimodal machine learning for predicting postoperative functional outcomes in surgically treated supratentorial deep intracerebral hemorrhage: a prospective multicenter study
A multimodal Random Forest model provided good discrimination for predicting postoperative functional outcomes in surgically treated sICH, and SHAP improved interpretability by quantifying feature contributions.
Authors
- Wei Xiong (ORCID: https://orcid.org/0000-0002-2532-2503)
- Qi He (ORCID: https://orcid.org/0000-0002-1533-7568)
- Xingwei Tan
- Yongbing Deng (ORCID: https://orcid.org/0000-0001-9575-9017)
- Yanyi Liu (ORCID: https://orcid.org/0000-0003-3568-7183)
- Yang Liu
- Lei Xu
- Min Cui
Institutions
- Chongqing University (CN)
- Stomatological Hospital of Chongqing Medical University (CN)
- Chongqing Emergency Medical Center (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2026-03-05
- DOI
- https://doi.org/10.3389/fneur.2026.1774621
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chongqing University