Explainable machine learning model based on clinical and radiological features for predicting hematoma expansion or rebleeding after decompressive craniectomy in traumatic brain injury: a bicentric cohort study
We developed and externally validated an interpretable XGBoost-based model for the early prediction of hematoma expansion or rebleeding after DC in patients with TBI. This tool offers practical clinical value for perioperative decision-making and targeted monitoring.
Authors
- Qi Mao (ORCID: https://orcid.org/0000-0002-6040-0116)
- Yue Wang (ORCID: https://orcid.org/0000-0003-0364-885X)
- Yun Ding
- Da Wu (ORCID: https://orcid.org/0009-0002-3522-3346)
- Kuan Jiang
- Liang Yao
- Minjie Jiang
Institutions
- Jiangsu University (CN)
- Yixing People's Hospital (CN)
- Wuxi Taihu Hospital (CN)
- Wuxi People's Hospital (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2026-02-12
- DOI
- https://doi.org/10.1097/js9.0000000000004933
- Primary Topic
- Traumatic Brain Injury and Neurovascular Disturbances
- Type
- article
- Field-Weighted Citation Impact
- 0.00