Predicting cerebral edema in patients with spontaneous intracerebral hemorrhage using machine learning
The GDBT model demonstrated the best performance in predicting 72-h changes in cerebral edema. It has the potential to assist clinicians in identifying high-risk patients and guiding clinical decision-making.
Authors
- Chengpeng Zhan (ORCID: https://orcid.org/0000-0003-2792-2083)
- Xin-Jiang Yan (ORCID: https://orcid.org/0009-0003-4074-2631)
- Guofeng Yu (ORCID: https://orcid.org/0000-0003-0006-3522)
- Hao Li (ORCID: https://orcid.org/0000-0001-5463-9529)
- Dandan Mao (ORCID: https://orcid.org/0000-0003-4704-226X)
- Jiangbao Xu
- Cuijie Yuan
- Qiutong Dong
Institutions
- Wenzhou Medical University (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2024-10-03
- DOI
- https://doi.org/10.3389/fneur.2024.1419608
- Citations
- 4
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 1.48