Data-driven machine-learning analysis of potential embolic sources in embolic stroke of undetermined source.
George NtaiosS F WengK PerlepeR AkyeaL CondonD LambrouG SirimarcoD StramboA EskandariE KaragkioziA VemmouEleni KorompokiE ManiosK MakaritsisK VemmosP MichelPublished in: European journal of neurology (2020)
This data-driven machine-learning analysis identified four clusters of ESUS that were strongly associated with arterial disease, atrial cardiopathy, PFO and left ventricular disease, respectively. More than half of the patients were assigned to the cluster associated with arterial disease.
Keyphrases
- machine learning
- left ventricular
- end stage renal disease
- atrial fibrillation
- newly diagnosed
- heart failure
- ejection fraction
- chronic kidney disease
- artificial intelligence
- deep learning
- drinking water
- prognostic factors
- acute myocardial infarction
- big data
- coronary artery disease
- risk assessment
- mitral valve
- left atrial
- blood brain barrier
- climate change
- cardiac resynchronization therapy
- patient reported