  Deep learning methods exhibit promising performance for predictive modeling
in healthcare, but two important challenges remain: -Data insufficiency:Often
in healthcare predictive modeling, the sample size is insufficient for deep
learning methods to achieve satisfactory results. -Interpretation:The
representations learned by deep learning methods should align with medical
knowledge. To address these challenges, we propose a GRaph-based Attention
Model, GRAM that supplements electronic health records (EHR) with hierarchical
information inherent to medical ontologies. Based on the data volume and the
ontology structure, GRAM represents a medical concept as a combination of its
ancestors in the ontology via an attention mechanism. We compared predictive
performance (i.e. accuracy, data needs, interpretability) of GRAM to various
methods including the recurrent neural network (RNN) in two sequential
diagnoses prediction tasks and one heart failure prediction task. Compared to
the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely
observed in the training data and 3% improved area under the ROC curve for
predicting heart failure using an order of magnitude less training data.
Additionally, unlike other methods, the medical concept representations learned
by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits
intuitive attention behaviors by adaptively generalizing to higher level
concepts when facing data insufficiency at the lower level concepts.
