According to the WHO report, 17.9 million people die each year from cardiovascular diseases (CVDs), an estimated 31% of all deaths worldwide, CVDs also has placed a heavy burden on patients and society. Although various risk factors that related to CVDs have been identified in many previous clinical studies, here still remain challenges to identify predictive features that can improve CVDs prediction and diagnosis model. Artificial intelligence/machine learning (AI/ML) techniques are known to be excellent at identifying important features and making a prediction. AI/ML with multiscale modeling approach is a rapidly growing field. It can help identify new targets and treatment strategies, and inform decision making for the benefit of human health. Multiscale modeling is developing models that represent multiple different scales (population, individual, organ, cellular, or molecular level) and how they interact with each other. Integrating AI/ML and multiscale modeling can be a powerful tool to help researchers to understand complex CVDs towards developing more accuracy predictive and diagnosis model and optimizing treatments of CVDs. This study aims to develop an integrated CVDs diagnosis and outcome prediction platform with AI/ML assisted analytical tools that can find the clinically meaningful pattern of CVDs and provide diagnosis, recommended treatment options with predicted outcome to better identify CVDs patients for better outcomes.
