Publication Name	World Journal of Clinical Cases
Manuscript ID	93182
Country	Italy

Received	2024-02-21 11:17
Peer-Review Started	2024-02-23 03:12
To Make the First Decision
Return for Revision	2024-03-06 21:17
Revised	2024-03-07 15:04
Second Decision	2024-03-27 02:36
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2024-03-27 05:28
Articles in Press	2024-03-27 05:28
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2024-04-17 06:16
Publish the Manuscript Online	2024-04-25 08:14

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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Website	http://www.wjgnet.com

Category	Multidisciplinary Sciences
Manuscript Type	Editorial
Article Title	Pioneering role of machine learning in unveiling intensive care unit-acquired weakness
Manuscript Source	Invited Manuscript
All Author List	Silvano Dragonieri
ORCID	Author(s) ORCID Number Silvano Dragonieri http://orcid.org/0000-0003-1563-6864
Funding Agency and Grant Number
Corresponding Author	Silvano Dragonieri, MD, PhD, Associate Professor, Department of Respiratory Diseases, University of Bari, Piazza Giulio Cesare 11, Bari 70124, Italy. silvano.dragonieri@uniba.it
Key Words	Intensive care unit-acquired weakness; Machine learning; Multilayer perceptron neural network; Predictive medicine; Interdisciplinary collaboration
Core Tip	This editorial leverages machine learning, specifically a multilayer perceptron neural network, to pinpoint key risk factors for intensive care unit-acquired weakness (ICU-AW), emphasizing the critical roles of ICU stay duration and mechanical ventilation. It heralds a paradigm shift towards data-driven, predictive medicine in critical care, advocating for the integration of artificial intelligence in clinical practices and interdisciplinary collaboration to enhance patient care outcomes.
Publish Date	2024-04-25 08:14
Citation	Dragonieri S. Pioneering role of machine learning in unveiling intensive care unit-acquired weakness. World J Clin Cases 2024; 12(13): 2157-2159
URL	https://www.wjgnet.com/2307-8960/full/v12/i13/2157.htm
DOI	https://dx.doi.org/10.12998/wjcc.v12.i13.2157

Full Article (PDF)	WJCC-12-2157-with-cover.pdf
Manuscript File	93182_Auto_Edited-YJP.docx
Answering Reviewers	93182-Answering reviewers.pdf
Audio Core Tip	93182-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	93182-Conflict-of-interest statement.pdf
Copyright License Agreement	93182-Copyright license agreement.pdf
Peer-review Report	93182-Peer-review(s).pdf
Scientific Misconduct Check	93182-Bing-Zheng XM-2.png
Scientific Misconduct Check	93182-CrossCheck.png
Scientific Editor Work List	93182-Scientific editor work list.pdf