Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China.

Journal: BMC public health
PMID: 40205363

Abstract

BACKGROUND: Allergic rhinitis is a common disease that can affect the health of patients and bring huge social and economic burdens. In this study, we developed a model to predict the incidence rate of allergic rhinitis so as to provide accurate information for the treatment, prevention, and control of allergic rhinitis.

Authors

  • Xiaofeng Fan
    Clinical Medicine Department of Hangzhou Normal University, Hangzhou, Zhejiang, People's Republic of China.
  • Liwei Chen
    Department of Automation, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
  • Wei Tang
    Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Lixia Sun
    Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China . Email: gxliu@ecust.edu.cn ; Email: ytang234@ecust.edu.cn ; ; Tel: +86-21-64250811.
  • Jie Wang
  • Shuhan Liu
    School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, China.
  • Sirui Wang
    Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan.
  • Kaijie Li
    Department of Otolaryngology, Taizhou Hospital, Taizhou, Zhejiang, People's Republic of China.
  • Mingwei Wang
    College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, People's Republic of China.
  • Yongran Cheng
    School of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, People's Republic of China. chengyr@zjams.com.cn.
  • Lili Dai
    The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China.

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