Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (10): 165-175.doi: 10.11896/jsjkx.220900177

• Artificial Intelligence • Previous Articles     Next Articles

Ventilator and Sedative Management in Networked ICUs Based on Federated Learning

CAO Linxiao1, LIU Jia2, ZHU Yifei3, ZHOU Haoquan4, GONG Wei4, YU Weihua5, LI Chaoyou6   

  1. 1 School of Data Science,University of Science and Technology of China,Hefei 230026,China
    2 Department of Pediatrics,Provincial Hospital Affiliated to Anhui Medical University,Hefei 230001,China
    3 University of Michigan-Shanghai Jiao Tong University Joint Institute,Shanghai Jiao Tong University,Shanghai 200240,China
    4 Department of Pediatrics,The First Affiliated Hospital of USTC,Hefei 230001,China
    5 Department of Nursing,The First People's Hospital of Hefei,Hefei 230061,China
    6 Department of Pediatrics,The First People's Hospital of Hefei,Hefei 230061,China
  • Received:2022-09-17 Revised:2023-02-10 Online:2023-10-10 Published:2023-10-10
  • About author:CAO Linxiao,born in 2000,postgra-duate.His main research interests include federated learning and reinforcement learning.ZHOU Haoquan,born in 1968,master,chief physician.His main research interests include childhood respiratory di-sease,bronchoscopy,and smart healthcare.
  • Supported by:
    Hefei Healthcare Grant(J2020Y03).

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Abstract: The proliferation of medical IoT devices and the abundance of medical data open up new possibilities for smart healthcare.Patients in the intensive care unit (ICU) rely on numerous medical IoT devices to continuously monitor and manage patients' health status.Among the common therapeutic interventions in ICUs,invasive mechanical ventilation and sedation are mostly administered to maintain patients' respiratory function and enhance the care quality.While the existing therapeutic interventions rely heavily on physician judgment.This paper proposes a data-driven optimal policy learning framework named MFed that allows distributed learning of optimal intervention policies on networked ICUs.A differentially private federated learning method is constructed to overcome privacy limitations in medical data.MFed further ensures worst-case performance with distributionally robust optimization and adaptively filters out noisy data.Extensive experiments on a real-world ICU dataset show that the proposed method improves accuracy by 36.75% compared to other state-of-the-art baselines.

Key words: Federated learning, Medical IoT, Distributionally robust optimization, Medical data noise, Medical data privacy

CLC Number: 

  • TP391
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