  We propose a novel and interpretable embedding method to represent the
international statistical classification codes of diseases and related health
problems (i.e., ICD codes). This method considers a self-attention mechanism
within the disease domain and a mutual-attention mechanism jointly between
diseases and procedures. This framework captures the clinical relationships
between the disease codes and procedures associated with hospital admissions,
and it predicts procedures according to diagnosed diseases. A self-attention
network is learned to fuse the embeddings of the diseases for each admission.
The similarities between the fused disease embedding and the procedure
embeddings indicate which procedure should potentially be recommended.
Additionally, when learning the embeddings of the ICD codes, the optimal
transport between the diseases and the procedures within each admission is
calculated as a regularizer of the embeddings. The optimal transport provides a
mutual-attention map between diseases and the procedures, which suppresses the
ambiguity within their clinical relationships. The proposed method achieves
clinically-interpretable embeddings of ICD codes, and outperforms
state-of-the-art embedding methods in procedure recommendation.
