Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism.

Journal: BMC medical informatics and decision making
Published Date: May 27, 2024

Abstract

BACKGROUND: Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing low prognosis risk patients with PTE accurately is significant for clinical treatment. This study evaluated the value of federated learning (FL) technology in PTE prognosis risk assessment while ensuring the security of clinical data.

Authors

  • Jun Zhou
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Yiyao Li
    Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China.
  • Yuqing Yang
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
  • Juhong Shi
    Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China. shijh@pumch.cn.

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