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With the wide application of electronic health records (EHR) in healthcarefacilities, health event prediction with deep learning has gained more and moreattention. A common feature of EHR data used for deep-learning-basedpredictions is historical diagnoses. Existing work mainly regards a diagnosisas an independent disease and does not consider clinical relations amongdiseases in a visit. Many machine learning approaches assume diseaserepresentations are static in different visits of a patient. However, in realpractice, multiple diseases that are frequently diagnosed at the same timereflect hidden patterns that are conducive to prognosis. Moreover, thedevelopment of a disease is not static since some diseases can emerge ordisappear and show various symptoms in different visits of a patient. Toeffectively utilize this combinational disease information and explore thedynamics of diseases, we propose a novel context-aware learning framework usingtransition functions on dynamic disease graphs. Specifically, we construct aglobal disease co-occurrence graph with multiple node properties for diseasecombinations. We design dynamic subgraphs for each patient's visit to leverageglobal and local contexts. We further define three diagnosis roles in eachvisit based on the variation of node properties to model disease transitionprocesses. Experimental results on two real-world EHR datasets show that theproposed model outperforms state of the art in predicting health events.

Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs