Pioneering role of machine learning in unveiling intensive care unit-acquired weakness

doi:10.12998/wjcc.v12.i13.2157

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May 6, 2024 (publication date) through Nov 24, 2024

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World Journal of Clinical Cases

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2307-8960

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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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

Core Tip

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.