ICDBigBird: A Contextual Embedding Model for ICD Code Classification
This work addresses the challenge of accurate ICD code assignment for healthcare reporting and management, which is crucial for clinical, operational, and financial decision-making, but it is incremental as it builds on existing BigBird and GCN methods.
The paper tackles the problem of classifying International Classification of Diseases (ICD) codes from clinical notes by addressing the limitation of existing contextual embedding models that cannot handle long documents, and it reports that their ICDBigBird model outperforms previous state-of-the-art models on a real-world dataset.
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.