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

Journal: IEEE journal of biomedical and health informatics
Published Date: Jul 2, 2024

Abstract

OBJECTIVE: The clinical course of COVID-19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID-19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID-19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time.

Authors

  • Jiaxin Cai
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Baichen Liu
  • Zhixi Wu
  • Shengjun Zhu
  • Qiliang Chen
  • Qing Lei
    Department of Orthopedics, Third Hospital of Changsha, Changsha 410015. lqing0504@hotmail.com.
  • Hongyan Hou
    Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. tjhouhongyan@tjh.tjmu.edu.cn.
  • Zhibin Guo
  • Hewei Jiang
  • Shujuan Guo
  • Feng Wang
    Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
  • Shengjing Huang
  • Shunzhi Zhu
  • Xionglin Fan
  • Shengce Tao

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