Automated ICD coding involves assigning standardized diagnostic codes to clinical narratives. The vast label space and extreme class imbalance continue to challenge precise prediction. To address these issues, LabGraph is introduced -- a unified framework that reformulates ICD coding as a graph generation task. By combining adversarial domain adaptation, graph-based reinforcement learning, and perturbation regularization, LabGraph effectively enhances model robustness and generalization. In addition, a label graph discriminator dynamically evaluates each generated code, providing adaptive reward feedback during training. Experiments on benchmark datasets demonstrate that LabGraph consistently outperforms previous approaches on micro-F1, micro-AUC, and P@K.

翻译：自动化ICD编码旨在为临床叙述分配标准化的诊断代码。庞大的标签空间与极端的类别不平衡持续对精确预测构成挑战。为解决这些问题，本文提出LabGraph——一个将ICD编码重新定义为图生成任务的统一框架。通过结合对抗性领域自适应、基于图的强化学习与扰动正则化，LabGraph有效增强了模型的鲁棒性与泛化能力。此外，标签图判别器动态评估每个生成的代码，在训练过程中提供自适应的奖励反馈。在基准数据集上的实验表明，LabGraph在micro-F1、micro-AUC和P@K指标上均持续优于先前方法。