LINKAGE: An approach for comprehensive risk prediction for care management

Abstract

Comprehensive risk assessment lies in the core of enabling proactive healthcare delivery systems. In recent years, data-driven predictive modeling approaches have been increasingly recognized as promising techniques to help enhance healthcare quality and reduce cost. In this paper, we propose a data-driven comprehensive risk prediction method, named Linkage, which can be used to jointly assess a set of associated risks in support of holistic care management. Our method can not only perform prediction but also discover the relationships among those risks. The advantages of the proposed model include: 1) It can leverage the relationship between risks and domains and achieve better risk prediction performance; 2) It provides a data-driven approach to understand relationship between risks; 3) It leverages the information between risk prediction and risk association learning to regulate the improvement on both parts; 4) It provides flexibility to incorporate domain knowledge in learning risk associations. We validate the effectiveness of the proposed model on synthetic data and a real-world healthcare survey data set.