  We present an end-to-end, interpretable, deep-learning architecture to learn
a graph kernel that predicts the outcome of chronic disease drug prescription.
This is achieved through a deep metric learning collaborative with a Support
Vector Machine objective using a graphical representation of Electronic Health
Records. We formulate the predictive model as a binary graph classification
problem with an adaptive learned graph kernel through novel cross-global
attention node matching between patient graphs, simultaneously computing on
multiple graphs without training pair or triplet generation. Results using the
Taiwanese National Health Insurance Research Database demonstrate that our
approach outperforms current start-of-the-art models both in terms of accuracy
and interpretability.
