Deep representation learning from electronic medical records identifies distinct symptom based subtypes and progression patterns for COVID-19 prognosis.

Journal: International journal of medical informatics
Published Date: Jul 20, 2024

Abstract

OBJECTIVE: Symptoms are significant kind of phenotypes for managing and controlling of the burst of acute infectious diseases, such as COVID-19. Although patterns of symptom clusters and time series have been considered the high potential prediction factors for the prognosis of patients, the elaborated subtypes and their progression patterns based on symptom phenotypes related to the prognosis of COVID-19 patients still need be detected. This study aims to investigate patient subtypes and their progression patterns with distinct features of outcome and prognosis.

Authors

  • Qiguang Zheng
    School of Computer and Information Technology, Beijing Jiaotong University, China.
  • Qifan Shen
    School of Computer and Information Technology, Beijing Jiaotong University, China.
  • Zixin Shu
  • Kai Chang
    Department of Electrical Engineering, Stanford University, Stanford, CA, United States.
  • Kunyu Zhong
    Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
  • Yuhang Yan
    School of Computer and Information Technology, Beijing Jiaotong University, China.
  • Jia Ke
    Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Jingjing Huang
    Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Rui Su
    Huami (Beijing) Information Technology Co. Ltd, Beijing, China.
  • Jianan Xia
    Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
  • Xuezhong Zhou
    School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

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