Estimating individualized effectiveness of receiving successful recanalization for ischemic stroke cases using machine learning techniques
This study highlights the potential of predictive ML techniques to estimate the individual effect of successful recanalization on ischemic stroke patients undergoing MT.
Authors
- Mate Elod Maros (ORCID: https://orcid.org/0000-0002-1589-8699)
- Helge C. Kniep (ORCID: https://orcid.org/0000-0001-5258-2370)
- Olga Lyashevska (ORCID: https://orcid.org/0000-0002-8686-8550)
- Vahid Farmani (ORCID: https://orcid.org/0009-0000-1705-7802)
- Jens Fiehler (ORCID: https://orcid.org/0000-0001-8533-7478)
- Liam Morris (ORCID: https://orcid.org/0000-0002-5690-8435)
- Fiona Malone (ORCID: https://orcid.org/0000-0001-7917-1854)
Institutions
- Universität Hamburg (DE)
- Netherlands eScience Center (NL)
- University Medical Center Hamburg-Eppendorf (DE)
- COPE Galway (IE)
Publication Details
- Journal
- Journal of Stroke and Cerebrovascular Diseases
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1016/j.jstrokecerebrovasdis.2025.108324
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00