Deep Machine Learning Might Aid in Combating Intensive Care Unit-Acquired Weakness

Secondary muscle weakness in critically ill patients like intensive care unit (ICU)-associated weakness is frequently noted in patients with prolonged mechanical ventilation and ICU stay. It can be a result of critical illness, myopathy, or neuropathy. Although ICU-acquired weakness (ICU-AW) has bee...

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Bibliographic Details
Published in:Curēus (Palo Alto, CA) CA), 2024-04, Vol.16 (4), p.e58963-e58963
Main Authors: Panda, Chinmaya K, Karim, Habib Md R
Format: Article
Language:English
Subjects:
Anesthesia
Biopsy
Clinical medicine
Clinical trials
Critical care
Electromyography
Enteral nutrition
Hyperglycemia
Illnesses
Intensive care
Machine learning
Meta-analysis
Morbidity
Mortality
Multiple organ dysfunction syndrome
Nutrition
Parenteral nutrition
Patients
Quality of life
Rehabilitation
Risk factors
Sepsis
Steroids
Systematic review
Ventilators
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Summary:Secondary muscle weakness in critically ill patients like intensive care unit (ICU)-associated weakness is frequently noted in patients with prolonged mechanical ventilation and ICU stay. It can be a result of critical illness, myopathy, or neuropathy. Although ICU-acquired weakness (ICU-AW) has been known for a while, there is still no effective treatment for it. Therefore, prevention of ICU-AW becomes the utmost priority, and knowing the risk factors is crucial. Nevertheless, the pathophysiology and the attributing causes are complex for ICU-AW, and proper delineation and formulation of a preventive strategy from such vast, multifaceted data are challenging. Artificial intelligence has recently helped healthcare professionals understand and analyze such intricate data through deep machine learning. Hence, using such a strategy also helps in knowing the risk factors and their weight as contributors, applying them in formulating a preventive path for ICU-AW worth trials.
ISSN:2168-8184
2168-8184
DOI:10.7759/cureus.58963