  The International Classification of Diseases (ICD) system is the
international standard for classifying diseases and procedures during a
healthcare encounter and is widely used for healthcare reporting and management
purposes. Assigning correct codes for clinical procedures is important for
clinical, operational, and financial decision-making in healthcare. Contextual
word embedding models have achieved state-of-the-art results in multiple NLP
tasks. However, these models have yet to achieve state-of-the-art results in
the ICD classification task since one of their main disadvantages is that they
can only process documents that contain a small number of tokens which is
rarely the case with real patient notes. In this paper, we introduce ICDBigBird
a BigBird-based model which can integrate a Graph Convolutional Network (GCN),
that takes advantage of the relations between ICD codes in order to create
'enriched' representations of their embeddings, with a BigBird contextual model
that can process larger documents. Our experiments on a real-world clinical
dataset demonstrate the effectiveness of our BigBird-based model on the ICD
classification task as it outperforms the previous state-of-the-art models.
