关键词: ICD疾病分类, 大标签空间, 多标签, 图注意力网络, 深度学习, 注意力重构

Abstract:

The International Classification of Diseases（ICD） is a classification system for health management and clinical purposes. This system is designed to map diagnoses, health conditions and therapeutic procedures to corresponding categories and assigning for these designated code. Towards solving the multi-label classification problem in the fairly large label space of the ICD, an end-to-end deep learning approach is proposed. The approach uses an improved graph attention network to model the label space, and then uses an attention-reconstruction based multi-label classifier for classification. During label space modeling, three different graph structures are constructed using the hierarchical structure of procedural codes in the ICD. The graph attention mechanism merges the structural information of the label space into the model to make use of label dependencies for multi-label classification. The approach proposed is closely related to the actual application scenario. Experiments show that in clinical ICD dataset, the proposed method has a significant improvement in classification performance.

Key words: ICD coding, large label space, multi-label, graph attention network, deep learning, attention-reconstruction