CRNNet: Copy Recurrent Neural Network Structure Network
Xiaofan Zhou Xunzhu Tang
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 Copy Recurrent Neural Network Structure Network (CRNNet). 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, demonstrating the effectiveness
of our proposed model. According to the ablation study, the proposed copy
mechanism plays a crucial role in detecting complicating diseases.