Publication Name	World Journal of Clinical Cases
Manuscript ID	93226
Country	Qatar

Category	Critical Care Medicine
Manuscript Type	Editorial
Article Title	Machine learning insights on intensive care unit-acquired weakness
Manuscript Source	Invited Manuscript
All Author List	Muad Abdi Hassan and Abdulqadir J Nashwan
Funding Agency and Grant Number
Corresponding Author	Abdulqadir J Nashwan, MSc, Research Scientist, Department of Nursing, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa
Key Words	Length of intensive care unit stay; Intensive care unit-acquired weakness; Machine learning; Likelihood factors; Precautionary measures
Core Tip	The study categorized patients into two groups: Intensive care unit-acquired weakness (ICU-AW) and non-ICU-AW, based on their condition on the 14th day post-ICU admission. The researchers collected data from the initial 14 d of the ICU stay, which included age, comorbidities, sedative and vasopressor dosages, duration of mechanical ventilation, length of the ICU stay, and rehabilitation therapy. They then examined the relationships between these variables and ICU-AW.
Citation	Hassan MA, Nashwan AJ. Machine learning insights on intensive care unit-acquired weakness. World J Clin Cases 2024; In press

Received	2024-02-22 07:46
Peer-Review Started	2024-02-22 07:46
To Make the First Decision
Return for Revision	2024-03-08 06:07
Revised	2024-03-14 18:24
Second Decision	2024-04-28 02:41
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2024-04-28 09:50
Articles in Press	2024-04-28 09:50
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2307-8960 (online)
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