Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.
Ulrich GüldenerThorsten KesslerMoritz von ScheidtJohann S HaweBeatrix GerhardDieter MaierMark LachmannKarl-Ludwig LaugwitzSalvatore CasseseAlbert W SchömigAdnan KastratiHeribert SchunkertPublished in: Journal of clinical medicine (2023)
The agnostic SOM-based approach identified-without clinical knowledge-even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.
Keyphrases
- coronary artery
- machine learning
- end stage renal disease
- antiplatelet therapy
- percutaneous coronary intervention
- coronary artery disease
- acute coronary syndrome
- chronic kidney disease
- healthcare
- ejection fraction
- optical coherence tomography
- public health
- computed tomography
- newly diagnosed
- st elevation myocardial infarction
- peritoneal dialysis
- st segment elevation myocardial infarction
- patient reported outcomes
- heart failure
- deep learning
- dna methylation
- pulmonary arterial hypertension
- bioinformatics analysis
- coronary artery bypass