SRPNet: stroke risk prediction based on two-level feature selection and deep fusion network
SRPNet can rapidly identify high-quality stroke risk factors, improve the accuracy of stroke prediction, and provide a powerful tool for clinical diagnosis.
Authors
- Zhi‐Ping Liu (ORCID: https://orcid.org/0000-0001-7742-9161)
- Na Yu (ORCID: https://orcid.org/0000-0002-1154-3348)
- Yang De Marinis (ORCID: https://orcid.org/0000-0002-8081-2142)
- Daoliang Zhang (ORCID: https://orcid.org/0009-0009-7682-6187)
- Rui Gao (ORCID: https://orcid.org/0000-0002-8476-0925)
- Xiaodan Yang
Institutions
- Shandong University (CN)
- Lund University (SE)
- Affiliated Hospital of Jining Medical University (CN)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2024-11-11
- DOI
- https://doi.org/10.3389/fphys.2024.1357123
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00