  The widespread application of Electronic Health Records (EHR) data in the
medical field has led to early successes in disease risk prediction using deep
learning methods. These methods typically require extensive data for training
due to their large parameter sets. However, existing works do not exploit the
full potential of EHR data. A significant challenge arises from the infrequent
occurrence of many medical codes within EHR data, limiting their clinical
applicability. Current research often lacks in critical areas: 1) incorporating
disease domain knowledge; 2) heterogeneously learning disease representations
with rich meanings; 3) capturing the temporal dynamics of disease progression.
To overcome these limitations, we introduce a novel heterogeneous graph
learning model designed to assimilate disease domain knowledge and elucidate
the intricate relationships between drugs and diseases. This model innovatively
incorporates temporal data into visit-level embeddings and leverages a
time-aware transformer alongside an adaptive attention mechanism to produce
patient representations. When evaluated on two healthcare datasets, our
approach demonstrated notable enhancements in both prediction accuracy and
interpretability over existing methodologies, signifying a substantial
advancement towards personalized and proactive healthcare management.
