  Deep learning architectures have an extremely high-capacity for modeling
complex data in a wide variety of domains. However, these architectures have
been limited in their ability to support complex prediction problems using
insurance claims data, such as readmission at 30 days, mainly due to data
sparsity issue. Consequently, classical machine learning methods, especially
those that embed domain knowledge in handcrafted features, are often on par
with, and sometimes outperform, deep learning approaches. In this paper, we
illustrate how the potential of deep learning can be achieved by blending
domain knowledge within deep learning architectures to predict adverse events
at hospital discharge, including readmissions. More specifically, we introduce
a learning architecture that fuses a representation of patient data computed by
a self-attention based recurrent neural network, with clinically relevant
features. We conduct extensive experiments on a large claims dataset and show
that the blended method outperforms the standard machine learning approaches.
