ICDBigBird: A Contextual Embedding Model for ICD Code Classification

• URL: http://arxiv.org/abs/2204.10408v1
• Date: Thu, 21 Apr 2022 20:59:56 GMT
• Title: ICDBigBird: A Contextual Embedding Model for ICD Code Classification
• Authors: George Michalopoulos, Michal Malyska, Nicola Sahar, Alexander Wong, Helen Chen
• Abstract summary: Contextual word embedding models have achieved state-of-the-art results in multiple NLP tasks. ICDBigBird is a BigBird-based model which can integrate a Graph Convolutional Network (GCN) Our experiments on a real-world clinical dataset demonstrate the effectiveness of our BigBird-based model on the ICD classification task.
• Score: 71.58299917476195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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