Copy Recurrent Neural Network Structure Network

Autor:	Zhou, Xiaofan, Tang, Xunzhu
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: Need modification
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.13250 Zobrazit plný text záznamu View this record from Arxiv