Construction of a machine learning-based prediction model for unfavorable discharge outcomes in patients with ischemic stroke
Our explainable ML model was not only successfully developed to accurately predict discharge outcomes in patients with ischemic stroke and it mitigated the concern of the "black-box" issue with an undirect interpretation of the ML technique.
Authors
- Xiuqing Zhang (ORCID: https://orcid.org/0000-0002-3416-501X)
- Jie Li (ORCID: https://orcid.org/0000-0002-6912-3346)
- Yuehua Chen (ORCID: https://orcid.org/0000-0003-2799-3703)
- Yuehong Wei (ORCID: https://orcid.org/0000-0002-3887-9079)
- Deng Qianyun
- Yuexin Mei
- zhou Meng
- Yuancheng He
- Xiaojuan Zhang (ORCID: https://orcid.org/0000-0001-5113-746X)
Institutions
- Sun Yat-sen University (CN)
- Chinese Academy of Sciences (CN)
- Guangzhou Institutes of Biomedicine and Health (CN)
- Guangzhou Center for Disease Control and Prevention (CN)
- Nanfang Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Guangdong Academy of Medical Sciences (CN)
- Guangdong Provincial People's Hospital (CN)
- Southern Medical University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-08-31
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e37179
- Citations
- 4
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.55
Funders
- National Natural Science Foundation of China
- Guangdong Provincial People's Hospital