  We introduce HTAD, a novel model for diagnosis prediction using Electronic
Health Records (EHR) represented as Heterogeneous Information Networks. Recent
studies on modeling EHR have shown success in automatically learning
representations of the clinical records in order to avoid the need for manual
feature selection. However, these representations are often learned and
aggregated without specificity for the different possible targets being
predicted. Our model introduces a target-aware hierarchical attention mechanism
that allows it to learn to attend to the most important clinical records when
aggregating their representations for prediction of a diagnosis.
  We evaluate our model using a publicly available benchmark dataset and
demonstrate that the use of target-aware attention significantly improves
performance compared to the current state of the art. Additionally, we propose
a method for incorporating non-categorical data into our predictions and
demonstrate that this technique leads to further performance improvements.
Lastly, we demonstrate that the predictions made by our proposed model are
easily interpretable.
