LGFat-RGCN: Faster Attention with Heterogeneous RGCN for Medical ICD Coding Generation

With the increasing volume of healthcare data, automated International Classification of Diseases (ICD) has become increasingly relevant and is frequently regarded as a medical multi-label prediction problem. Current methods struggle to accurately classify medical diagnosis texts that represent deep and sparse categories. Unlike these works that model the label with code hierarchy or description for label prediction, we argue that the label generation with structural information can provide more comprehensive knowledge based on the observation that label synonyms and parent-child relationships in vary from their context in clinical contexts. In this study, we introduce LGFat-RGCN, a heterogeneous graph model with improved attention for automated ICD coding. Notably, our approach represents the model to consider this task as a labelled graph generation problem. Our enhanced attention mechanism boosts the model's capacity to learn from multi-relational heterogeneous graph representations. Additionally, we propose a discriminator for labelled graphs (LG) that computes the reward for each ICD code in the labelled graph generator. Our experimental findings demonstrate that our proposed model significantly outperforms all existing strong baseline methods and attains the best performance on three benchmark datasets.