Double decoupled network for imbalanced obstetric intelligent diagnosis

Kunli Zhang; Shuai Zhang; Yu Song; Linkun Cai; Bin Hu; Kunli Zhang; Shuai Zhang; Yu Song; Linkun Cai; Bin Hu

doi:10.3934/mbe.2022467

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 10: 10006-10021. doi: 10.3934/mbe.2022467

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Research article Special Issues

Double decoupled network for imbalanced obstetric intelligent diagnosis

1.
College of Computer Intelligence, Zhengzhou University, Zhengzhou, China
2.
Pengcheng laboratory, Shenzhen, Guangdong, China
3.
College of Biological Science and Medical Engineering, Beihang University, Beijing, China

Academic Editor: Zhichang Zhang

Received: 30 May 2022 Revised: 05 July 2022 Accepted: 07 July 2022 Published: 13 July 2022

Electronic Medical Record (EMR) is the data basis of intelligent diagnosis. The diagnosis results of an EMR are multi-disease, including normal diagnosis, pathological diagnosis and complications, so intelligent diagnosis can be treated as multi-label classification problem. The distribution of diagnostic results in EMRs is imbalanced. And the diagnostic results in one EMR have a high coupling degree. The traditional rebalancing methods does not function effectively on highly coupled imbalanced datasets. This paper proposes Double Decoupled Network (DDN) based intelligent diagnosis model, which decouples representation learning and classifier learning. In the representation learning stage, Convolutional Neural Networks (CNN) is used to learn the original features of the data. In the classifier learning stage, a Decoupled and Rebalancing highly Imbalanced Labels (DRIL) algorithm is proposed to decouple the highly coupled diagnostic results and rebalance the datasets, and then the balanced datasets is used to train the classifier. This paper evaluates the proposed DDN using Chinese Obstetric EMR (COEMR) datasets, and verifies the effectiveness and universality of the model on two benchmark multi-label text classification datasets: Arxiv Academic Papers Datasets (AAPD) and Reuters Corpus1 (RCV1). Demonstrating the effectiveness of the proposed methods is an imbalanced obstetric EMRs. The accuracy of DDN model on COEMR, AAPD and RCV1 datasets is 84.17, 86.35 and 93.87% respectively, which is higher than the current optimal experimental results.
- imbalanced classification,
- decoupled,
- rebalancing methods,
- intelligent diagnosis
Citation: Kunli Zhang, Shuai Zhang, Yu Song, Linkun Cai, Bin Hu. Double decoupled network for imbalanced obstetric intelligent diagnosis[J]. Mathematical Biosciences and Engineering, 2022, 19(10): 10006-10021. doi: 10.3934/mbe.2022467

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Abstract

Electronic Medical Record (EMR) is the data basis of intelligent diagnosis. The diagnosis results of an EMR are multi-disease, including normal diagnosis, pathological diagnosis and complications, so intelligent diagnosis can be treated as multi-label classification problem. The distribution of diagnostic results in EMRs is imbalanced. And the diagnostic results in one EMR have a high coupling degree. The traditional rebalancing methods does not function effectively on highly coupled imbalanced datasets. This paper proposes Double Decoupled Network (DDN) based intelligent diagnosis model, which decouples representation learning and classifier learning. In the representation learning stage, Convolutional Neural Networks (CNN) is used to learn the original features of the data. In the classifier learning stage, a Decoupled and Rebalancing highly Imbalanced Labels (DRIL) algorithm is proposed to decouple the highly coupled diagnostic results and rebalance the datasets, and then the balanced datasets is used to train the classifier. This paper evaluates the proposed DDN using Chinese Obstetric EMR (COEMR) datasets, and verifies the effectiveness and universality of the model on two benchmark multi-label text classification datasets: Arxiv Academic Papers Datasets (AAPD) and Reuters Corpus1 (RCV1). Demonstrating the effectiveness of the proposed methods is an imbalanced obstetric EMRs. The accuracy of DDN model on COEMR, AAPD and RCV1 datasets is 84.17, 86.35 and 93.87% respectively, which is higher than the current optimal experimental results.

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