Leveraging machine learning for enhanced and interpretable risk prediction of venous thromboembolism in acute ischemic stroke care
This study provides an accurate and interpretable method to predict VTE risk in patients with AIS using the GBM model, potentially improving early detection rates and reducing morbidity. Further validation is needed to assess its broader clinical applicability.
Authors
- Qingshi Zhao (ORCID: https://orcid.org/0009-0001-1015-2337)
- Zhihuan Li (ORCID: https://orcid.org/0009-0009-1754-7516)
- Youli Jiang (ORCID: https://orcid.org/0000-0002-6856-5177)
- Ao Li (ORCID: https://orcid.org/0000-0001-5061-9694)
- Rong Li (ORCID: https://orcid.org/0000-0003-3971-1670)
- Yanfeng Li (ORCID: https://orcid.org/0000-0002-4041-2259)
- Guisu Li
Institutions
- Central South University (CN)
- ShenZhen People’s Hospital (CN)
- Second Xiangya Hospital of Central South University (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-03-18
- DOI
- https://doi.org/10.1371/journal.pone.0302676
- Citations
- 4
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 5.42