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 [DOI: 10.12998/wjcc.v12.i21.4455]
Corresponding Author of This Article
Elie Bou Sanayeh, MD, MBA, Doctor, Department of Medicine, Staten Island University Hospital, 475 Seaview Avenue, Staten Island, NY 10305, United States. elie.h.bousanayeh@gmail.com
Research Domain of This Article
Critical Care Medicine
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Clin Cases. Jul 26, 2024; 12(21): 4455-4459 Published online Jul 26, 2024. doi: 10.12998/wjcc.v12.i21.4455
Advancing critical care recovery: The pivotal role of machine learning in early detection of intensive care unit-acquired weakness
Georges Khattar, Elie Bou Sanayeh
Georges Khattar, Elie Bou Sanayeh, Department of Medicine, Staten Island University Hospital, Staten Island, NY 10305, United States
Author contributions: Khattar G conceptualized the study; Khattar G and Bou Sanayeh E designed the methodology; Khattar G participated in the formal investigation and literature review; Khattar G wrote the original draft of the manuscript; Bou Sanayeh E edited the manuscript for important intellectual content and supervised the study; all authors read and agreed to the published version of the manuscript.
Conflict-of-interest statement: The authors declare having no conflicts of interest.
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/
Corresponding author: Elie Bou Sanayeh, MD, MBA, Doctor, Department of Medicine, Staten Island University Hospital, 475 Seaview Avenue, Staten Island, NY 10305, United States. elie.h.bousanayeh@gmail.com
Received: March 5, 2024 Revised: May 14, 2024 Accepted: May 27, 2024 Published online: July 26, 2024 Processing time: 116 Days and 19.8 Hours
Core Tip
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.
