ICDBigBird: A Contextual Embedding Model for ICD Code Classification
PROCEEDINGS OF THE 21ST WORKSHOP ON BIOMEDICAL LANGUAGE PROCESSING (BIONLP 2022)(2022)
摘要
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.
更多查看译文
关键词
icdbigbird code classification,contextual
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要