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© The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The accurate classification of International Classification of Diseases (ICD) codes is a complex and critical multi-label task in clinical documentation, involving the assignment of diagnostic codes to medical discharge summaries. Existing automated methods face challenges due to the sparsity and nuanced nature of medical text, while traditional backpropagation-based models often lack flexibility and robustness. To address these issues, we propose Labeled Graph Generation with Node Representation Grasp (LGG-NRGrasp), an advanced adversarial learning framework that models ICD coding as a labeled graph generation problem. By leveraging a hierarchical structure to refine feature learning, our approach addresses the issue of over-smoothing in deep graph neural networks. A key innovation of LGG-NRGrasp is the integration of adversarial reinforcement learning and domain adaptation techniques, which enhance its ability to generalize across heterogeneous datasets. Extensive evaluations on benchmark datasets indicate that LGG-NRGrasp markedly surpasses leading models, exhibiting enhanced performance and dependability in automated ICD coding.

Details

Title
Breaking barriers in ICD classification with a robust graph neural network for hierarchical coding
Author
Xi, Suyang 1 ; Shi, Jiesen 1 ; Yan, Jiachen 2 ; Lin, MingJing 1 ; Zhou, Xinyi 3 ; Cheng, Yuan 1 ; Ding, Hong 1 ; Kang, Chia Chao 1 

 Xiamen University Malaysia, School of Artificial Intelligence and Robotics, Sepang, Malaysia (GRID:grid.503008.e) (ISNI:0000 0004 7423 0677) 
 Xiamen University Malaysia, School of Communication, Sepang, Malaysia (GRID:grid.503008.e) (ISNI:0000 0004 7423 0677) 
 Hainan University, School of Life and Health Sciences, Haikou, China (GRID:grid.428986.9) (ISNI:0000 0001 0373 6302) 
Pages
25676
Publication year
2025
Publication date
2025
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3230337192
Copyright
© The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.