MedSeq2Seq: A Medical Knowledge Enriched Sequence to Sequence Learning Model for COVID-19 Diagnosis

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2021
Major/Subject
Mcode
Degree programme
Language
en
Pages
4
3181-3184
Series
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Abstract
The COVID-19 pandemic has had a severe impact on humans' lives and and healthcare systems worldwide. How to early, fastly and accurately diagnose infected patients via multimodal learning is now a research focus. The central challenges in this task mainly lie on multi-modal data representation and multi-modal feature fusion. To solve such challenges, we propose a medical knowledge enriched multi-modal sequence to sequence learning model, termed MedSeq2Seq. The key components include two attention mechanisms, viz. intra-modal (Ia) and inter-model (Ie) attentions, and a medical knowledge augmentation mechanism. The former two mechanisms are to learn multi-modal refined representation, while the latter aims to incorporate external medical knowledge into the proposed model. The experimental results show the effectiveness of the proposed MedSeq2Seq framework over state-of-the-art baselines with a significant improvement of 1%-2%.
Description
Funding Information: Acknowledgment This work is supported by National Science Foundation of China under grant No. 62006212, the fund of State Key Lab. for Novel Software Technology in Nanjing University under grant No.KFKT2021B41. Publisher Copyright: © 2021 IEEE.
Keywords
attention mechanism, coronavirus epidemic, COVID-19 diagnose, deep learning, Seq2Seq learning
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Citation
Zhang, Y, Rong, L, Li, X, Tiwari, P, Zheng, Q & Liang, H 2021, MedSeq2Seq: A Medical Knowledge Enriched Sequence to Sequence Learning Model for COVID-19 Diagnosis . in Y Huang, L Kurgan, F Luo, X T Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 . IEEE, pp. 3181-3184, IEEE International Conference on Bioinformatics and Biomedicine, Virtual, Online, United States, 09/12/2021 . https://doi.org/10.1109/BIBM52615.2021.9669854