Simultaneous Modeling of Multiple Complications for Risk Profiling in Diabetes Care

Type 2 diabetes mellitus (T2DM) is a chronic disease that o‰en results in multiple complications. Risk prediction and pro€ling of T2DM complications is critical for healthcare professionals to design personalized treatment plans for patients in diabetes care for improved outcomes. In this paper, we study the risk of developing complications a‰er the initial T2DM diagnosis from longitudinal patient records. We propose a novel multi-task learning approach to simultaneously model multiple complications where each task corresponds to the risk modeling of one complication. Speci€cally, the proposed method strategically captures the relationships (1) between the risks of multiple T2DM complications, (2) between the di‚erent risk factors, and (3) between the risk factor selection paŠerns. Œe method uses coecient shrinkage to identify an informative subset of risk factors from high-dimensional data, and uses a hierarchical Bayesian framework to allow domain knowledge to be incorporated as priors. Œe proposed method is favorable for healthcare applications because in additional to improved prediction performance, relationships among the di‚erent risks and risk factors are also identi€ed. Extensive experimental results on a large electronic medical claims database show that the proposed method outperforms state-of-the-art models by a signi€cant margin. Furthermore, we show that the risk associations learned and the risk factors identi€ed lead to meaningful clinical insights. CCS CONCEPTS •Information systems→ Data mining; •Applied computing → Health informatics;