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Australian Trends in Procedural Characteristics and Outcomes in Patients Undergoing Percutaneous Coronary Intervention for ST-Elevation Myocardial Infarction Correction of a pathogenic gene mutation in human embryos ACC临床简报:新型冠状病毒对心脏的影响(2019-nCoV) Age-specific gender differences in early mortality following ST-segment elevation myocardial infarction in China Blood CSF1 and CXCL12 as Causal Mediators of Coronary Artery Disease Clinical Implications of Periprocedural Myocardial Injury in Patients Undergoing Percutaneous Coronary Intervention for Chronic Total Occlusion: Role of Antegrade and Retrograde Crossing Techniques Precision Medicine in TAVR: How to Select the Right Device for the Right Patient Association of All-Cause and Cardiovascular Mortality With High Levels of Physical Activity and Concurrent Coronary Artery Calcification CSC Expert Consensus on Principles of Clinical Management of Patients with Severe Emergent Cardiovascular Diseases during the COVID-19 Epidemic Contemporary Approach to Coronary Bifurcation Lesion Treatment

Review Article2020 Jul 16;229:1-17.

JOURNAL:Am Heart J . Article Link

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

CR Olsen, RJ Mentz, KJ Anstrom et al. Keywords: machine learning; artificial intelligence;

ABSTRACT

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.