State‐of‐the‐Art Machine Learning Techniques Aiming to Improve Patient Outcomes Pertaining to the Cardiovascular System
Rahul K. Sevakula, Wan‐Tai M. Au‐Yeung, Jagmeet P. Singh, E. Kevin Heist, Eric M. Isselbacher, Antonis A. Armoundas
Abstract
W ith the digitization of all records and processes, and prevalence of cloud-driven services and Internet of Things, today's era can truly be considered as an era of data. Machine learning (ML) and artificial intelligence (AI) skills are among the most sought-after skills today. McKinsey Global Institute research suggests that 45% of workplace activities in corporations could be automated with current technologies; 80% of that is attributable to existing ML capabilities, and breakthroughs in natural language processing could further the impact. 1 Gartner forecasts that large-scale data-driven analytics could lead to huge benefits in health care; in the United States, where healthcare spending is 18% of gross domestic product, up to US$600 per person could be saved annually. Gartner also forecasts that data-driven insights for demand-supply matching could create an economic impact of $850 billion to $2.5 trillion. 2 International Data Corporation forecasts that spending on AI and ML will grow to $79.2 billion by 2022, with a compound annual growth rate of 38% between the 2018 and 2022 period.