Publication Name	World Journal of Clinical Cases
Manuscript ID	93289
Country	Colombia

Received	2024-02-23 20:23
Peer-Review Started	2024-02-23 20:23
To Make the First Decision
Return for Revision	2024-03-09 02:49
Revised	2024-03-09 18:52
Second Decision	2024-03-22 02:43
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2024-03-22 07:08
Articles in Press	2024-03-22 07:08
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-04-10 04:14
Publish the Manuscript Online	2024-04-16 07:57

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/
Copyright	© The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
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Website	http://www.wjgnet.com

Category	Computer Science, Artificial Intelligence
Manuscript Type	Editorial
Article Title	Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach
Manuscript Source	Invited Manuscript
All Author List	Carlos Martin Ardila, Daniel González-Arroyave and Mateo Zuluaga-Gómez
ORCID	Author(s) ORCID Number Carlos Martin Ardila http://orcid.org/0000-0002-3663-1416 Daniel González-Arroyave http://orcid.org/0000-0002-2162-8484 Mateo Zuluaga-Gómez http://orcid.org/0000-0002-5768-4939
Funding Agency and Grant Number
Corresponding Author	Carlos Martin Ardila, DDS, PhD, Doctor, Doctor, Postdoc, Professor, Science Editor, Department of Basic Sciences, Biomedical Stomatology Research Group, University of Antioquia, Medellín 52-59, Colombia. martinardila@gmail.com
Key Words	Intensive care units; Intensive care unit-acquired weakness; Risk factors; Machine learning; Computer neural network
Core Tip	Predicting intensive care unit-acquired weakness (ICUAW) is crucial for improving patient outcomes. This editorial presents the potential of machine learning, specifically the multilayer perceptron neural network model, in predicting ICUAW. Insights into ICUAW risk factors and guides clinical decision-making in critical care are offered. The importance of developing accurate and reliable predictive models to improve patient outcomes in the intensive care unit setting is also emphasized.
Publish Date	2024-04-16 07:57
Citation	Ardila CM, González-Arroyave D, Zuluaga-Gómez M. Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach. World J Clin Cases 2024; 12(12): 2023-2030
URL	https://www.wjgnet.com/2307-8960/full/v12/i12/2023.htm
DOI	https://dx.doi.org/10.12998/wjcc.v12.i12.2023

Full Article (PDF)	WJCC-12-2023-with-cover.pdf
Manuscript File	93289_Auto_Edited-YJP.docx
Answering Reviewers	93289-Answering reviewers.pdf
Audio Core Tip	93289-Audio core tip.ogg
Conflict-of-Interest Disclosure Form	93289-Conflict-of-interest statement.pdf
Copyright License Agreement	93289-Copyright license agreement.pdf
Non-Native Speakers of English Editing Certificate	93289-Language certificate.pdf
Peer-review Report	93289-Peer-review(s).pdf
Scientific Misconduct Check	93289-CrossCheck.png
Scientific Misconduct Check	93289-Bing-Zheng XM-2.png
Scientific Editor Work List	93289-Scientific editor work list.pdf
CrossCheck Report	93289-CrossCheck report.pdf