The target of Electronic Health Record (EHR) coding is to find the diagnostic codes according to the EHRs. In previous research, researchers have preferred to do multi-classification on the EHR coding task; most of them encode the EHR first and then process it to get the probability of each code based on the EHR representation. However, the question of complicating diseases is neglected among all these methods. In this paper, we propose a novel EHR coding framework, which is the first attempt at detecting complicating diseases, called ComplicaCode. This method refers to the idea of adversarial learning; a Path Generator and a Path Discriminator are designed to more efficiently finish the task of EHR coding. We propose a copy module to detect complicating diseases; by the proposed copy module and the adversarial learning strategy, we identify complicating diseases efficiently. Extensive experiments show that our method achieves a 57.30\% ratio of complicating diseases in predictions, and achieves the state-of-the-art performance among cnn-based baselines, it also surpasses transformer methods in the complication detection task, demonstrating the effectiveness of our proposed model. According to the ablation study, the proposed copy mechanism plays a crucial role in detecting complicating diseases.

翻译：电子健康记录（EHR）编码的目标是根据EHR找到对应的诊断编码。在以往的研究中，研究者们倾向于将EHR编码任务视为多分类问题；他们通常先对EHR进行编码，然后基于其表示处理得到每个编码的概率。然而，所有这些方法都忽视了疾病并发症的问题。本文提出了一种新颖的EHR编码框架，这是首次尝试检测并发症的模型，称为ComplicaCode。该方法借鉴了对抗学习的思想；设计了一个路径生成器和一个路径判别器，以更高效地完成EHR编码任务。我们提出了一个复制模块来检测并发症；通过所提出的复制模块和对抗学习策略，我们有效地识别了并发症。大量实验表明，我们的方法在预测中实现了57.30%的并发症比例，并在基于CNN的基线模型中取得了最先进的性能，同时在并发症检测任务中超越了Transformer方法，证明了我们提出模型的有效性。根据消融研究，所提出的复制机制在检测并发症中起到了关键作用。