Are neural networks the ultimate risk prediction models in patients at high risk of acute myocardial infarction?

Identifying and phenotyping patients at risk of developing major cardiovascular events is an ongoing research priority. With an abundance of variables and confounders, the current prediction tools based on linear multivariate regression models are becoming outpowered by the emerging machine learning algorithms. These algorithms have the potential to identify, with a higher prediction power healthy patients at risk, prompting preventive interventions, such as early screening imaging tools or medical therapy (e.g. antiplatelet therapy).