  Predicting medications is a crucial task in many intelligent healthcare
systems. It can assist doctors in making informed medication decisions for
patients according to electronic medical records (EMRs). However, medication
prediction is a challenging data mining task due to the complex relations
between medical codes. Most existing studies focus on utilizing inherent
relations between homogeneous codes of medical ontology graph to enhance their
representations using supervised methods, and few studies pay attention to the
valuable relations between heterogeneous or homogeneous medical codes from
history EMRs, which further limits the prediction performance and application
scenarios. Therefore, to address these limitations, this paper proposes
KnowAugNet, a multi-sourced medical knowledge augmented medication prediction
network which can fully capture the diverse relations between medical codes via
multi-level graph contrastive learning framework. Specifically, KnowAugNet
first leverages the graph contrastive learning using graph attention network as
the encoder to capture the implicit relations between homogeneous medical codes
from the medical ontology graph and obtains the knowledge augmented medical
codes embedding vectors. Then, it utilizes the graph contrastive learning using
a weighted graph convolutional network as the encoder to capture the
correlative relations between homogeneous or heterogeneous medical codes from
the constructed medical prior relation graph and obtains the relation augmented
medical codes embedding vectors. Finally, the augmented medical codes embedding
vectors and the supervised medical codes embedding vectors are retrieved and
input to the sequential learning network to capture the temporal relations of
medical codes and predict medications for patients.
