Published 2024
| Version v9
Preprint
Open
Stroke Audit Machine Learning (SAMueL-2)
Creators
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1.
University of Exeter
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2.
Northumbria University
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3.
University of Oxford
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4.
Royal Devon and Exeter Hospital
Description
Using large-scale observational data and machine learning, thrombolysis, in real world use, was found to have at least as much benefit as predicted by the thrombolysis clinical trial meta-analysis. Both qualitative research and machine learning revealed significant between-hospital variation in which patients receive thrombolysis, which is leading to significant between-hospital variation in thrombolysis use and outcomes. 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_Synopisis.pdf
Files
(9.9 MB)
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Additional details
Dates
- Available
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2024