Methods for estimating resting energy expenditure in intensive care patients: A comparative study of predictive equations with machine learning and deep learning approaches.

Journal: Computer methods and programs in biomedicine
PMID: 39954654

Abstract

BACKGROUND: Accurate estimation of resting energy expenditure (REE) is critical for guiding nutritional therapy in critically ill patients. While indirect calorimetry (IC) is the gold standard for REE measurement, it is not routinely feasible in clinical settings due to its complexity and cost. Predictive equations (PEs) offer a simpler alternative but are often inaccurate in critically ill populations. While recent advancements in machine learning (ML) and deep learning (DL) offer potential for improving REE estimation by capturing complex relationships between physiological variables, these approaches have not yet been widely applied or validated in critically ill populations.

Authors

  • Christopher Yew Shuen Ang
    School of Engineering, Monash University Malaysia, Selangor, Malaysia.
  • Mohd Basri Mat Nor
    Department of Anaesthesiology and Intensive Care, Kulliyah of Medicine, International Islamic University of Malaysia, Kuantan 25200, Malaysia.
  • Nur Sazwi Nordin
    Kulliyyah of Medicine, International Islamic University Malaysia, Pahang, Malaysia.
  • Thant Zin Kyi
    Innure Biotechnologies Sdn Bhd, Petaling Jaya, Selangor, Malaysia.
  • Ailin Razali
    Kulliyyah of Medicine, International Islamic University Malaysia, Pahang, Malaysia.
  • Yeong Shiong Chiew
    School of Engineering, Monash University Malaysia, Selangor, Malaysia; Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand. Electronic address: chiew.yeong.shiong@monash.edu.

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