Background The promise of precision population health includes the ability to use robust patient data to tailor prevention and care to specific groups. Advanced analytics may allow for automated detection of clinically informative subgroups that account for clinical, genetic, and environmental variability. This study sought to evaluate whether unsupervised machine learning approaches could interpret heterogeneous and missing clinical data to discover clinically important coronary artery disease subgroups.
View Article and Find Full Text PDFThis national cohort study examines changes in the number of vascular surgical procedures completed in the US before and during the COVID-19 pandemic.
View Article and Find Full Text PDFAtherosclerosis is the process underlying heart attack and stroke. Despite decades of research, its pathogenesis remains unclear. Dogma suggests that atherosclerotic plaques expand primarily via the accumulation of cholesterol and inflammatory cells.
View Article and Find Full Text PDF