Shifting Towards Teaching How to Communicate Statistics in Medical Education.

Despite the widespread inclusion of statistics in medical school curricula as per the Liaison Committee on Medical Education requirements, the statistical competency among medical students and clinicians remains low. A 2007 study of 277 medical residents revealed only 41.1% scored correctly on a statistical knowledge survey, with minimal understanding of key concepts such as confidence intervals and adjusted odds ratios.

Survival machine learning methods for mortality prediction after heart transplantation in the contemporary era.

Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms-Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr.

Clinical and Genomic Prediction of Coronary Artery Disease Subtypes.

Background: Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alternative preventions and treatments. However, so far, there have been no systematic efforts to predict CAD subtypes using clinical and genetic factors.