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Classification of patients with embolic stroke of undetermined source into cardioembolic and non-cardioembolic profile subgroups.

Max Christian MartinThorsten SichtermannKolja SchürmannPardes HabibMartin WiesmannJörg B SchulzOmid NikoubashmanJoão PinhoArno Reich
Published in: European journal of neurology (2022)
An ML model based on baseline demographic and laboratory variables was able to classify ESUS patients into cardioembolic or non-cardioembolic profile groups and predicted that 40% of the ESUS patients had a cardioembolic profile.
Keyphrases
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • chronic kidney disease
  • prognostic factors
  • machine learning
  • atrial fibrillation
  • deep learning
  • patient reported outcomes
  • subarachnoid hemorrhage