Electronic Health Record (EHR) coding involves automatically classifying EHRs into diagnostic codes. While most previous research treats this as a multi-label classification task, generating probabilities for each code and selecting those above a certain threshold as labels, these approaches often overlook the challenge of identifying complex diseases. In this study, our focus is on detecting complication diseases within EHRs. We propose a novel coarse-to-fine ICD path generation framework called the Copy Recurrent Neural Network Structure Network (CRNNet), which employs a Path Generator (PG) and a Path Discriminator (PD) for EHR coding. By using RNNs to generate sequential outputs and incorporating a copy module, we efficiently identify complication diseases. Our method achieves a 57.30\% ratio of complex diseases in predictions, outperforming state-of-the-art and previous approaches. Additionally, through an ablation study, we demonstrate that the copy mechanism plays a crucial role in detecting complex diseases.

提出了一种利用复制递归神经网络（RNN）结构的路径生成器和路径判别器进行疾病分类的粗到细的ICD路径生成框架（CRNNet），在识别并发症时具有更高的效率，57.30％的复杂疾病预测比现有技术和以前的方法表现更好，同时通过消融研究证明了复制机制在检测复杂疾病中发挥了关键作用。

复制递归神经网络结构网络