Dragonieri S. Pioneering role of machine learning in unveiling intensive care unit-acquired weakness. World J Clin Cases 2024; 12(13): 2157-2159 [PMID: 38808351 DOI: 10.12998/wjcc.v12.i13.2157]
Corresponding Author of This Article
Silvano Dragonieri, MD, PhD, Associate Professor, Department of Respiratory Diseases, University of Bari, Piazza Giulio Cesare 11, Bari 70124, Italy. silvano.dragonieri@uniba.it
Research Domain of This Article
Multidisciplinary Sciences
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. May 6, 2024; 12(13): 2157-2159 Published online May 6, 2024. doi: 10.12998/wjcc.v12.i13.2157
Pioneering role of machine learning in unveiling intensive care unit-acquired weakness
Silvano Dragonieri
Silvano Dragonieri, Department of Respiratory Diseases, University of Bari, Bari 70124, Italy
Author contributions: Dragonieri S conceived and wrote the entire manuscript.
Conflict-of-interest statement: The author has no conflicts of interest to declare.
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: Silvano Dragonieri, MD, PhD, Associate Professor, Department of Respiratory Diseases, University of Bari, Piazza Giulio Cesare 11, Bari 70124, Italy. silvano.dragonieri@uniba.it
Received: February 21, 2024 Peer-review started: February 23, 2024 First decision: March 6, 2024 Revised: March 7, 2024 Accepted: March 27, 2024 Article in press: March 27, 2024 Published online: May 6, 2024 Processing time: 63 Days and 21 Hours
Abstract
In the research published in the World Journal of Clinical Cases, Wang and Long conducted a quantitative analysis to delineate the risk factors for intensive care unit-acquired weakness (ICU-AW) utilizing advanced machine learning methodologies. The study employed a multilayer perceptron neural network to accurately predict the incidence of ICU-AW, focusing on critical variables such as ICU stay duration and mechanical ventilation. This research marks a significant advancement in applying machine learning to clinical diagnostics, offering a new paradigm for predictive medicine in critical care. It underscores the importance of integrating artificial intelligence technologies in clinical practice to enhance patient management strategies and calls for interdisciplinary collaboration to drive innovation in healthcare.
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.
