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.
翻译:国际疾病分类系统(疾病分类系统)是保健遇到时对疾病和程序进行分类的国际标准,广泛用于保健报告和管理目的,为临床程序指定正确的代码对于医疗的临床、业务和财政决策十分重要,背景化的词嵌入模型在多项国家疾病分类任务中取得了最新成果。然而,这些模型尚未在疾病分类任务中取得最新结果,因为它们的一个主要缺点是,它们只能处理含有少量物品的文件,而实际病人笔记很少如此。在本文件中,我们引入了基于大鸟的模型,该模型可以综合利用国际疾病分类编码之间的关系,以创建“丰富”的嵌入模型,而大鸟可以处理大文件。我们关于真实世界临床数据集的实验表明我们基于大鸟的分类模型在ICD分类任务上的效力,因为它超越了以前的状态-艺术模型。