将疾病编码转化为图生成任务，提升精准度与泛化能力。
Graph Inference Towards ICD Coding
- 把ICD编码看作图生成，融合对抗域适应与图强化学习。
- 在多个数据集上微平均F1、AUC和P@K均优于现有方法。
- 适合医疗文本自动化标注与复杂标签系统研究者。
自动化ICD编码旨在为临床文本分配标准化诊断代码。庞大的标签空间和极端类别不平衡仍是精确预测的主要挑战。为此，本文提出LabGraph——一种统一框架，将ICD编码重新定义为图生成任务。通过结合对抗域适应、基于图的强化学习与扰动正则化，LabGraph显著提升模型鲁棒性与泛化能力。此外，引入标签图判别器动态评估每个生成代码，训练中提供自适应奖励反馈。在基准数据集上的实验表明，LabGraph在微平均F1、微平均AUC和P@K指标上持续优于先前方法。
原文摘要 · Abstract (English)
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.
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