Towards Automatic ICD Coding via Label Graph Generation

Abstract

Automatic International Classification of Disease (ICD) coding, a system for assigning proper codes to a given clinical text, has received increasing attention. Previous studies have focused on formulating the ICD coding task as a multi-label prediction approach, exploring the relationship between clinical texts and ICD codes, parent codes and child codes, and siblings. However, the large search space of ICD codes makes it difficult to localize target labels. Moreover, there exists a great unbalanced distribution of ICD codes at different levels. In this work, we propose LabGraph, which transfers ICD coding into a graph generation problem. Specifically, we present adversarial domain adaptation training algorithms, graph reinforcement algorithms, and adversarial perturbation regularization. Then, we present a discriminator for label graphs that calculates the reward for each ICD code in the generator label graph. LabGraph surpasses existing state-of-the-art approaches on core assessment measures such as micro-F1, micro-AUC, and P@K, leading to the formation of a new state-of-the-art study.