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	He XY, Zhao YH, Wan QW, Tang FS. Unraveling intensive care unit-acquired weakness: Unveiling significant risk factors and preemptive strategies through machine learning. World J Clin Cases 2024; In press

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 Company 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

ISSN	2307-8960 (online)
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