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Articles Published Processes
7/1/2024 1:38:21 PM | Browse: 74 | Download: 373
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Received |
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2024-03-05 10:07 |
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Peer-Review Started |
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2024-03-05 10:07 |
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First Decision by Editorial Office Director |
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2024-05-04 05:52 |
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Return for Revision |
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2024-05-04 05:52 |
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Revised |
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2024-05-14 07:04 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2024-05-27 02:46 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2024-05-27 08:31 |
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Articles in Press |
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2024-05-27 08:31 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2024-05-31 09:14 |
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Publish the Manuscript Online |
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2024-07-01 13:38 |
| 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/ |
| 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
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| 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 |
Advancing critical care recovery: The pivotal role of machine learning in early detection of intensive care unit-acquired weakness
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Georges Khattar and Elie Bou Sanayeh |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Elie Bou Sanayeh, MD, Doctor, Doctor, Department of Medicine, Staten Island University Hospital, 475 Seaview Avenue, Staten Island, NY 10305, United States. elie.h.bousanayeh@gmail.com |
| Key Words |
Predictive modeling; Personalized intervention strategies; Patient outcomes; Neural network models; Intensive care unit-acquired weakness; Early detection; Critical illness polyneuropathy; Critical illness myopathy |
| Core Tip |
Intensive care unit-acquired weakness (ICU-AW) significantly impacts patient recovery and healthcare costs, affecting a broad spectrum of critically ill patients. Timely detection and prevention are essential in managing this condition effectively. Early and precise prediction, facilitated by advanced methodologies such as neural network models exemplified by Wang et al's study, represent pivotal advancements in addressing ICU-AW. These models offer enhanced early detection and facilitate tailored intervention strategies, underscoring the imperative for ongoing research to refine their accuracy and applicability. This editorial emphasizes the critical role of predictive tools in improving patient outcomes in critical care, highlighting the urgency of developing and validating sophisticated models to proactively manage ICU-AW. |
| Publish Date |
2024-07-01 13:38 |
| Citation |
Khattar G, Bou Sanayeh E. Advancing critical care recovery: The pivotal role of machine learning in early detection of intensive care unit-acquired weakness. World J Clin Cases 2024; 12(21): 4455-4459 |
| URL |
https://www.wjgnet.com/2307-8960/full/v12/i21/4455.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v12.i21.4455 |
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