Publication Name	World Journal of Clinical Cases
Manuscript ID	93307
Country	Taiwan

Received	2024-02-24 11:27
Peer-Review Started	2024-02-24 11:27
To Make the First Decision
Return for Revision	2024-04-02 19:27
Revised	2024-04-23 03:47
Second Decision	2024-04-25 02:59
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-04-25 10:27
Articles in Press	2024-04-25 10:27
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-05-30 08:00
Publish the Manuscript Online	2024-06-18 09:29

ISSN	2307-8960 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
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Category	Health Care Sciences & Services
Manuscript Type	Editorial
Article Title	Unveiling significant risk factors for intensive care unit-acquired weakness: Advancing preventive care
Manuscript Source	Invited Manuscript
All Author List	Chun-Yao Cheng, Wen-Rui Hao and Tzu-Hurng Cheng
Funding Agency and Grant Number	Funding Agency Grant Number China Medical University CMU111-MF-102
Corresponding Author	Tzu-Hurng Cheng, PhD, Professor, Department of Biochemistry, School of Medicine, College of Medicine, China Medical University, No. 91 Xueshi Road, North District, Taichung 404333, Taiwan. thcheng@mail.cmu.edu.tw
Key Words	Intensive care unit-acquired weakness; Artificial intelligence; Machine learning; Neural network; Risk factors; Prediction; Critical care
Core Tip	This editorial comment on the published article related to the potential of artificial intelligence (AI) and machine learning in predicting and mitigating intensive care unit-acquired weakness (ICU-AW) in critically ill patients. By identifying key risk factors and developing a predictive model, clinicians can optimize patient care and improve outcomes. Early prediction and intervention based on AI-driven insights may lead to more personalized and effective strategies for preventing ICU-AW.
Publish Date	2024-06-18 09:29
Citation	<p>Cheng CY, Hao WR, Cheng TH. Unveiling significant risk factors for intensive care unit-acquired weakness: Advancing preventive care. <i>World J Clin Cases</i> 2024; 12(18): 3288-3290</p>
URL	https://www.wjgnet.com/2307-8960/full/v12/i18/3288.htm
DOI	https://dx.doi.org/10.12998/wjcc.v12.i18.3288

Full Article (PDF)	WJCC-12-3288-with-cover.pdf
Manuscript File	93307_Auto_Edited-JLW.docx
Answering Reviewers	93307-answering-reviewers.pdf
Audio Core Tip	93307-audio.mp3
Conflict-of-Interest Disclosure Form	93307-conflict-of-interest-statement.pdf
Copyright License Agreement	93307-copyright-assignment.pdf
Approved Grant Application Form(s) or Funding Agency Copy of any Approval Document(s)	93307-foundation-statement.pdf
Non-Native Speakers of English Editing Certificate	93307-non-native-speakers.pdf
Peer-review Report	93307-peer-reviews.pdf
Scientific Misconduct Check	93307-scientific-misconduct-check.png
Scientific Editor Work List	93307-scientific-editor-work-list.pdf
CrossCheck Report	93307-crosscheck-report.pdf