  With the wide application of electronic health records (EHR) in healthcare
facilities, health event prediction with deep learning has gained more and more
attention. A common feature of EHR data used for deep-learning-based
predictions is historical diagnoses. Existing work mainly regards a diagnosis
as an independent disease and does not consider clinical relations among
diseases in a visit. Many machine learning approaches assume disease
representations are static in different visits of a patient. However, in real
practice, multiple diseases that are frequently diagnosed at the same time
reflect hidden patterns that are conducive to prognosis. Moreover, the
development of a disease is not static since some diseases can emerge or
disappear and show various symptoms in different visits of a patient. To
effectively utilize this combinational disease information and explore the
dynamics of diseases, we propose a novel context-aware learning framework using
transition functions on dynamic disease graphs. Specifically, we construct a
global disease co-occurrence graph with multiple node properties for disease
combinations. We design dynamic subgraphs for each patient's visit to leverage
global and local contexts. We further define three diagnosis roles in each
visit based on the variation of node properties to model disease transition
processes. Experimental results on two real-world EHR datasets show that the
proposed model outperforms state of the art in predicting health events.
