SAMueL

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Published 2024 | Version v7
Preprint Open

Stroke Audit Machine Learning (SAMueL-2)

Authors/Creators

  • 1. ROR icon University of Exeter
  • 2. ROR icon Northumbria University
  • 3. ROR icon University of Oxford
  • 4. ROR icon Royal Devon and Exeter Hospital

Description

We combined qualitative research and large-scale observational data with machine learning on use of, and outcomes from, thrombolysis. Both qualitative research and machine learning revealed significant between-hospital variation in which patients receive thrombolysis. Machine learning revealed that who will benefit from thrombolysis is patient-specific, and not easily captured in a simple medicine use label, but we found overall that stroke teams with a higher willingness to use thrombolysis are predicted to be generating better patient outcomes at a population level.

Files

samuel_2_summary_paper.pdf

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