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