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6/18/2024 9:29:50 AM | Browse: 37 | Download: 153
Publication Name World Journal of Clinical Cases
Manuscript ID 93226
Country Qatar
Received
2024-02-22 07:46
Peer-Review Started
2024-02-22 07:46
To Make the First Decision
Return for Revision
2024-03-08 06:07
Revised
2024-03-14 18:24
Second Decision
2024-04-28 02:41
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief
2024-04-28 09:50
Articles in Press
2024-04-28 09:50
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript
2024-06-03 05:39
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/
Copyright © The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Article Reprints For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
Permissions For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
Publisher Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Website http://www.wjgnet.com
Category Critical Care Medicine
Manuscript Type Editorial
Article Title Machine learning insights on intensive care unit-acquired weakness
Manuscript Source Invited Manuscript
All Author List Muad Abdi Hassan and Abdulqadir J Nashwan
ORCID
Author(s) ORCID Number
Muad Abdi Hassan http://orcid.org/0009-0007-6128-1351
Abdulqadir J Nashwan http://orcid.org/0000-0003-4845-4119
Funding Agency and Grant Number
Corresponding Author Abdulqadir J Nashwan, MSc, Research Scientist, Department of Nursing, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa
Key Words Length of intensive care unit stay; Intensive care unit-acquired weakness; Machine learning; Likelihood factors; Precautionary measures
Core Tip The study categorized patients into two groups: Intensive care unit-acquired weakness (ICU-AW) and non-ICU-AW, based on their condition on the 14th day post-ICU admission. The researchers collected data from the initial 14 d of the ICU stay, which included age, comorbidities, sedative and vasopressor dosages, duration of mechanical ventilation, length of the ICU stay, and rehabilitation therapy. They then examined the relationships between these variables and ICU-AW.
Publish Date 2024-06-18 09:29
Citation <p>Hassan MA, Nashwan AJ. Machine learning insights on intensive care unit-acquired weakness. <i>World J Clin Cases</i> 2024; 12(18): 3285-3287</p>
URL https://www.wjgnet.com/2307-8960/full/v12/i18/3285.htm
DOI https://dx.doi.org/10.12998/wjcc.v12.i18.3285
Full Article (PDF) WJCC-12-3285-with-cover.pdf
Manuscript File 93226_Auto_Edited-JLW.docx
Answering Reviewers 93226-answering-reviewers.pdf
Audio Core Tip 93226-audio.m4a
Conflict-of-Interest Disclosure Form 93226-conflict-of-interest-statement.pdf
Copyright License Agreement 93226-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate 93226-non-native-speakers.pdf
Peer-review Report 93226-peer-reviews.pdf
Scientific Misconduct Check 93226-scientific-misconduct-check.png
Scientific Misconduct Check 93226-scientific-misconduct-check.png
Scientific Editor Work List 93226-scientific-editor-work-list.pdf
CrossCheck Report 93226-crosscheck-report.pdf