Probability-Biased Attention over Directed Bipartite Graphs for Long-Tail ICD Coding

The proposed method for ICD coding, which utilizes a Directed Bipartite Graph Encoder, reflects a broader trend in AI research focusing on complex multi-label classification tasks. Similar to the challenges in Fine-Grained Visual Classification (FGVC), where subtle inter-class differences complicate classification, the ICD coding task also grapples with a vast label space and the prevalence of rare codes. The integration of probability-based biases in attention mechanisms, as seen in the proposed method, aligns with emerging strategies in AI that enhance model performance by leveraging co-occurrence data, a theme echoed in the ReactionTeam study on divergent thinking in synthetic chemistry.