Copy Recurrent Neural Network Structure Network

• URL: http://arxiv.org/abs/2305.13250v2
• Date: Mon, 26 Jun 2023 19:11:30 GMT
• Title: Copy Recurrent Neural Network Structure Network
• Authors: Xiaofan Zhou, Xunzhu Tang
• Abstract summary: We propose a novel ICD path generation framework called Copy Recurrent Neural Network Structure Network (CRNNet) 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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