Title: VGBNet: a disease diagnosis model based on local and global information fusion

Authors: Yong Li; Xinyu Zhao; Manfu Ma; Qiang Zhang; Hai Jia; Xia Wang

Addresses: College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China ' College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China ' College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China ' College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China ' Department of Pharmacy, Gansu Provincial Hospital, Lanzhou, 730000, China ' Department of Pharmacy, Gansu Provincial Hospital, Lanzhou, 730000, China

Abstract: There are significant differences in the data volume of different types of diseases in the electronic medical record (EMR) data. In addition, the mainstream auxiliary diagnosis and prediction models have two shortcomings. They either only pay attention to the local features of medical records and ignore the global features, or only pay attention to the global features and ignore the local features. In response to these problems, we propose a method of fusion random resampling to balance the data set, Using graph convolutional neural network (GCN) to extract global features, combined with a bidirectional self-attention network (BERT), a VGBNet model is proposed to link local and global features to achieve diagnosis and prediction of diseases. A large number of experiments show that compared with the state-of-the-art models, the VGBNet model has improved F1 value and accuracy. This is of great significance to the precise auxiliary diagnosis.

Keywords: unbalanced data set; disease prediction; GCN; graph convolutional neural network; attention mechanism.

DOI: 10.1504/IJCSM.2023.130687

International Journal of Computing Science and Mathematics, 2023 Vol.17 No.2, pp.107 - 122

Received: 25 Jun 2021
Accepted: 14 Aug 2021
Published online: 03 May 2023 *

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