Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study
The ML algorithm effectively classified patients with LAA, SVO, and CE, demonstrating superior classification performance compared to the LR model. The optimal ML model can identify potential CE patients among SUE patients. These newly identified predictive factors may complement the existing etiological classification system, enabling clinicians to promptly categorize stroke patients' etiology and initiate optimal strategies for secondary prevention.
Authors
- Siding Chen (ORCID: https://orcid.org/0009-0005-8669-7842)
- Xiaomeng Yang (ORCID: https://orcid.org/0000-0001-5484-9665)
- Yongjun Wang (ORCID: https://orcid.org/0000-0001-5040-8926)
- Yong Jiang (ORCID: https://orcid.org/0000-0001-7700-6054)
- Hongqiu Gu
- Yanzhao Wang (ORCID: https://orcid.org/0000-0002-3975-5449)
- Zhe Xu (ORCID: https://orcid.org/0000-0001-5157-570X)
Institutions
- Chonnam National University (KR)
- Capital Medical University (CN)
- Beijing Tian Tan Hospital (CN)
- National Clinical Research Center for Digestive Diseases (CN)
- Center for Excellence in Brain Science and Intelligence Technology (CN)
- Chinese Institute for Brain Research (CN)
- Beijing Tsinghua Chang Gung Hospital (CN)
- Beijing Fengtai Hospital (CN)
- Renmin University of China (CN)
- Beihang University (CN)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2024-09-10
- DOI
- https://doi.org/10.1186/s12874-024-02331-1
- Citations
- 5
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.93
Funders
- National Natural Science Foundation of China
- Chinese Academy of Medical Sciences