Predicting 3-month poor functional outcomes of acute ischemic stroke in young patients using machine learning
ML outperformed logistic regression, where XGBoost the boost was the best model for predicting poor functional outcomes in young AIS patients. It is important to consider living alone conditions with high severity scores to improve stroke prognosis.
Authors
- Siding Chen (ORCID: https://orcid.org/0009-0005-8669-7842)
- Xiaomeng Yang (ORCID: https://orcid.org/0000-0001-5484-9665)
- Lamia Mbarek (ORCID: https://orcid.org/0000-0003-1800-0108)
- Yongjun Wang (ORCID: https://orcid.org/0000-0001-5040-8926)
- Zhe Xu (ORCID: https://orcid.org/0000-0003-2559-109X)
- Yuesong Pan (ORCID: https://orcid.org/0000-0003-3082-6789)
- Aoming Jin (ORCID: https://orcid.org/0000-0002-7221-5444)
- Yong Jiang (ORCID: https://orcid.org/0009-0004-1379-2171)
- Xia Meng (ORCID: https://orcid.org/0000-0002-4918-0845)
Institutions
- Chonnam National University (KR)
- Capital Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Beijing Tian Tan Hospital (CN)
- National Clinical Research Center for Digestive Diseases (CN)
- Center for Excellence in Brain Science and Intelligence Technology (CN)
- Beijing Academy of Artificial Intelligence (CN)
- Chinese Institute for Brain Research (CN)
- Beijing Tsinghua Chang Gung Hospital (CN)
- Beihang University (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2024-10-10
- DOI
- https://doi.org/10.1186/s40001-024-02056-3
- Citations
- 7
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 2.71
Funders
- National Natural Science Foundation of China
- Chinese Academy of Medical Sciences