Publication Name	World Journal of Clinical Cases
Manuscript ID	89589
Country	China

Received	2023-11-06 13:41
Peer-Review Started	2023-11-06 13:42
First Decision by Editorial Office Director	2024-01-09 09:17
Return for Revision	2024-01-09 09:17
Revised	2024-01-20 05:05
Publication Fee Transferred
Second Decision by Editor	2024-02-18 01:54
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2024-02-18 06:57
Articles in Press	2024-02-18 06:57
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-02-20 06:11
Publish the Manuscript Online	2024-03-01 02:45

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: http://creativecommons.org/Licenses/by-nc/4.0/
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Category	Critical Care Medicine
Manuscript Type	Case Control Study
Article Title	Significant risk factors for intensive care unit-acquired weakness: A processing strategy based on repeated machine learning
Manuscript Source	Unsolicited Manuscript
All Author List	Ling Wang and Deng-Yan Long
ORCID	Author(s) ORCID Number Ling Wang http://orcid.org/0000-0001-7114-0519
Funding Agency and Grant Number	Funding Agency Grant Number Guizhou Province High-Level Innovative Talent Training Program Qiannan Thousand Talents [2022]201701 Science and Technology Support Program of Qiandongnan Prefecture Qiandongnan Sci-Tech Support [2021]12
Corresponding Author	Ling Wang, FRCS (Hon), PhD, Additional Professor, Chief Physician, Intensive Care Unit, People's Hospital of Qiandongnan Miao and Dong Autonomous Prefecture, No. 31 Shaoshan South Road, Kaili 556000, Guizhou Province, China. 463082910@qq.com
Key Words	Intensive care unit-acquired weakness; Risk factors; Machine learning; Prevention; Strategies
Core Tip	The study, utilizing machine learning, identified key risk factors for intensive care unit-acquired weakness (ICU-AW). Findings emphasized the significant impact of length of ICU stay and the duration of mechanical ventilation. Other factors, including age, medication dosage, and specific disease states, were also implicated. The study employed a multilayer perceptron neural network model with an impressive area under receiver operating characteristic curve of 0.941, sensitivity of 92.2%, and specificity of 82.7%. The results underscore the importance of decreasing length of ICU stay and the duration of mechanical ventilation as a primary strategy in preventing ICU-AW, when feasible.
Publish Date	2024-03-01 02:45
Citation	Wang L, Long DY. Significant risk factors for intensive care unit-acquired weakness: a processing strategy based on repeated machine learning. World J Clin Cases 2024; 12(7): 1235-1242
URL	https://www.wjgnet.com/2307-8960/full/v12/i7/1235.htm
DOI	https://dx.doi.org/10.12998/wjcc.v12.i7.1235

Full Article (PDF)	WJCC-12-1235-with-cover.pdf
STROBE Statement	89589-STROBE statement.pdf
Manuscript File	89589_Auto_Edited-YJP.docx
Answering Reviewers	89589-Answering reviewers.pdf
Audio Core Tip	89589-Audio core tip.mp3
Biostatistics Review Certificate	89589-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	89589-Conflict-of-interest statement.pdf
Copyright License Agreement	89589-Copyright license agreement.pdf
Signed Informed Consent Form(s) or Document(s)	89589-Informed consent statement.pdf
Institutional Review Board Approval Form or Document	89589-Institutional review board statement.pdf
Non-Native Speakers of English Editing Certificate	89589-Language certificate.pdf
Peer-review Report	89589-Peer-review(s).pdf
Scientific Editor Work List	89589-Scientific editor work list.pdf
CrossCheck Report	89589-CrossCheck report.pdf