With cardiovascular disease continuing to be the leading cause of death worldwide, more accurate AI-driven risk predictions could help doctors personalize heart care earlier and prevent serious cardiac events. That’s according to the University of Missouri School of Medicine where one researcher is exploring ways of using machine learning to accomplish that end goal. Fares Alahdab is the study author who says his model assigned patient risk of a major adverse cardiac event more accurately that other predictive models that interpret data. The report was recently published ahead of print in the Journal of Nuclear Cardiology which is the official journal of the American Society of Nuclear Cardiology. (link: Improving prognostic risk assessment of cardiovascular events with machine learning: An evaluation using positron emission tomography myocardial perfusion imaging – ScienceDirect)