Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

arXiv:2403.01533v1 Announce Type: new
Abstract: Precise estimation of cardiac patients' current and future comorbidities is an important factor in prioritizing continuous physiological monitoring and new therapies. ML models have shown satisfactory performance in short-term mortality prediction of patients with heart disease, while their utility in long-term predictions is limited. This study aims to investigate the performance of tree-based ML models on long-term mortality prediction and the effect of two recently introduced biomarkers on long-term mortality. This study utilized publicly available …

abstract arxiv clinical continuous cs.ai cs.lg current data disease eess.sp future heart disease long-term machine machine learning ml models monitoring mortality patients performance prediction type