Early prediction of high-cost inpatients with ischemic heart disease using network analytics and machine learning

Although identifying high-cost inpatients with ischemic heart disease (IHD) at the point of admission is helpful for timely intervention and reducing costs, it is a difficult task due to the limited data available at such an early stage of treatment. The aim of this study was to tackle this challenge by integrating network analytics with