Machine learning for early dynamic prediction of functional outcome after stroke
The performance of our transformer model demonstrates the potential of machine learning models integrating clinical, physiological, imaging, and biological variables over time after stroke. The clinical applicability of our model is further strengthened by access to hourly updated predictions along with accompanying explanations.
Authors
- Guillaume Leclerc
- Julian Klug (ORCID: https://orcid.org/0000-0002-4849-9811)
- Elisabeth Dirren (ORCID: https://orcid.org/0000-0001-7757-4967)
- Emmanuel Carrera (ORCID: https://orcid.org/0000-0003-0045-5382)
Institutions
- University Hospital of Geneva (CH)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1038/s43856-024-00666-w
- Citations
- 18
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 6.96
Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Schweizerische Herzstiftung