Enhancing ischemic stroke management: leveraging machine learning models for predicting patient recovery after Alteplase treatment
Our findings highlight the potential of machine learning in optimizing ischemic stroke treatment outcomes. Random Forest, in particular, proved effective as a decision-support tool, offering clinicians valuable insights for more tailored treatment approaches.
Authors
- Babak Khorsand (ORCID: https://orcid.org/0000-0002-1585-3909)
- Maryam Zand (ORCID: https://orcid.org/0000-0001-6833-1859)
- Abdoreza Ghoreishi (ORCID: https://orcid.org/0000-0003-0623-7749)
- Atena Vaghf
- Vahide Salimi
Institutions
- Zanjan University of Medical Sciences (IR)
- Shahrekord University of Medical Sciences (IR)
- University of California, Irvine (US)
- University of California System (US)
Publication Details
- Journal
- Brain Injury
- Published
- 2025-02-28
- DOI
- https://doi.org/10.1080/02699052.2025.2472188
- Citations
- 3
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 4.06