  Convolutional neural network (CNN) and recurrent neural network (RNN) models
have become the mainstream methods for relation classification. We propose a
unified architecture, which exploits the advantages of CNN and RNN
simultaneously, to identify medical relations in clinical records, with only
word embedding features. Our model learns phrase-level features through a CNN
layer, and these feature representations are directly fed into a bidirectional
gated recurrent unit (GRU) layer to capture long-term feature dependencies. We
evaluate our model on two clinical datasets, and experiments demonstrate that
our model performs significantly better than previous single-model methods on
both datasets.
