A systematic review of machine learning models for management, prediction and classification of ARDS.

Journal: Respiratory research
Published Date: Jun 4, 2024

Abstract

AIM: Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS.

Authors

  • Tu K Tran
    Department of Engineering and Science, University of Oxford, Oxford, UK. tu.tran@wolfson.ox.ac.uk.
  • Minh C Tran
    Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK.
  • Arun Joseph
    Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK.
  • Phi A Phan
    Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK.
  • Vicente Grau
    Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
  • Andrew D Farmery
    Nuffield Division of Anaesthetics, University of Oxford, Oxford, UK.

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