Publication Name	World Journal of Clinical Cases
Manuscript ID	94417
Country	China

Category	Computer Science, Artificial Intelligence
Manuscript Type	Editorial
Article Title	Unraveling intensive care unit-acquired weakness: Unveiling significant risk factors and preemptive strategies through machine learning
Manuscript Source	Invited Manuscript
All Author List	Xiao-Yu He, Yi-Huan Zhao, Qian-Wen Wan and Fu-Shan Tang
Funding Agency and Grant Number
Corresponding Author	Fu-Shan Tang, PhD, Professor, Department of Clinical Pharmacy, Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, No. 6 Xuefu West Road, Xinpu New District, Zunyi 563006, Guizhou Province, China. fstang@vip.163.com
Key Words	Intensive care unit-acquired weakness; Risk factors; Machine learning; Clinical medicine; Treatment decision
Core Tip	This editorial emphasizes the importance of recognizing the risk factors linked to intensive care unit-acquired weakness and highlights the vital role of machine learning in identifying and managing these factors to improve patient outcomes and enhance the quality of care in clinical settings.
Citation	<p>He XY, Zhao YH, Wan QW, Tang FS. Unraveling intensive care unit-acquired weakness: Unveiling significant risk factors and preemptive strategies through machine learning. <i>World J Clin Cases</i> 2024; 12(35): 6760-6763</p>

Received	2024-03-18 12:51
Peer-Review Started	2024-03-18 12:51
To Make the First Decision
Return for Revision	2024-08-09 16:57
Revised	2024-08-22 09:37
Second Decision	2024-09-04 02:37
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-09-04 07:13
Articles in Press	2024-09-04 07:13
Publication Fee Transferred
Edit the Manuscript by Language Editor	2024-09-07 14:35
Typeset the Manuscript	2024-09-19 09:47

ISSN	2307-8960 (online)
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