Deep Learning for Dynamic Prognostic Prediction in Minimally Invasive Surgery for Intracerebral Hemorrhage: Model Development and Validation Study
The MultiStep Transformer model proposed in this study can effectively use imbalanced data to construct a model. It possesses good dynamic prediction ability for short-term and long-term survival and functional outcome of patients with ICH undergoing MIS, providing a novel tool for individualized assessment of prognosis among patients with ICH undergoing MIS.
Authors
- Yuhao Sun
- Danyang Chen
- Jian Shi
- Ping Zhang
- Yingxin Tang
- Zhouping Tang
- Chao Pan
- Qing Ye
- Jingxuan Wang
Institutions
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2026-01-07
- DOI
- https://doi.org/10.2196/86327
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00