  Type 2 diabetes mellitus (T2DM) is a chronic disease that often results in
multiple complications. Risk prediction and profiling 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 after 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. Specifically, the proposed method
strategically captures the relationships (1) between the risks of multiple T2DM
complications, (2) between the different risk factors, and (3) between the risk
factor selection patterns. The method uses coefficient 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. The proposed method is favorable for healthcare applications because in
additional to improved prediction performance, relationships among the
different risks and risk factors are also identified. Extensive experimental
results on a large electronic medical claims database show that the proposed
method outperforms state-of-the-art models by a significant margin.
Furthermore, we show that the risk associations learned and the risk factors
identified lead to meaningful clinical insights.
