Towards Automatic ICD Coding via Label Graph Generation

Peng Nie, Huanqin Wu and Zhanchuan Cai ()
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Peng Nie: School of Computer, Guangdong University of Science and Technology, Dongguan 523083, China
Huanqin Wu: School of Computer, Guangdong University of Science and Technology, Dongguan 523083, China
Zhanchuan Cai: School of Computer Science and Engineering, Macau University of Science and Technology, Macau 999078, China

Mathematics, 2024, vol. 12, issue 15, 1-15

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.

Keywords: graph convolutional network; faster attention; transfer learning; ICD coding; adversarial domain adaptation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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