A Predictive Analytics Methodology to Assess and Optimize Readmission Risk in Heart Failure Patients

Pavitra Krishnaswamy, Savitha Ramasamy, Abdullah Al-Dujaili, Srinath Sridharan, Geraldine Goh, Shao Chuen Tong, Khin Chaw Yu Aung, Gerard Leong Kui Toh, Michael Ross Macdonald, Sheldon Lee Shao Guang, Yan Cao, Suresh Sundaram, Chow Wai Leng

Abstract: We describe a predictive modeling and decision support framework that integrates machine learning and optimization for personalized clinical decision support. We pilot the approach on data from a congestive heart failure patient cohort, and demonstrate the ability to predict and optimize readmission risk in a clinically meaningful manner.