Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time-Reference-Cited by-同舟云学术

Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time

Published:2024-07 Issue:7 Volume:28 Page:4204-4215
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Cai Jiaxin¹^ORCID,Li Yang²^ORCID,Liu Baichen³^ORCID,Wu Zhixi³^ORCID,Zhu Shengjun¹^ORCID,Chen Qiliang³^ORCID,Lei Qing⁴^ORCID,Hou Hongyan⁴^ORCID,Guo Zhibin³^ORCID,Jiang Hewei²^ORCID,Guo Shujuan²^ORCID,Wang Feng⁴^ORCID,Huang Shengjing⁵^ORCID,Zhu Shunzhi³^ORCID,Fan Xionglin⁶^ORCID,Tao Shengce²^ORCID

Affiliation:

1. School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China

2. Shanghai Center for Systems Biomedicine, Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China

3. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

4. Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

5. School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen, China

6. School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Funder

Natural Science Foundation of Fujian Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6221020/10582272/10488697.pdf?arnumber=10488697

Reference30 articles.

1. Translation: Diagnosis and Treatment Protocol for COVID-19 (Trial Version 9)

2. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

3. Effective Doses in Radiology and Diagnostic Nuclear Medicine: A Catalog

4. COVID-19 diagnosis by routine blood tests using machine learning

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