He XY, Zhao YH, Wan QW, Tang FS. Intensive care unit-acquired weakness: Unveiling significant risk factors and preemptive strategies through machine learning. World J Clin Cases 2024; 12(35): 6760-6763 [DOI: 10.12998/wjcc.v12.i35.6760]
Corresponding Author of This Article
Fu-Shan Tang, PhD, Professor, Department of Clinical Pharmacy, Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, No. 6 Xuefu West Road, Xinpu New District, Zunyi 563006, Guizhou Province, China. fstang@vip.163.com
Research Domain of This Article
Computer Science, Artificial Intelligence
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/
Xiao-Yu He, Yi-Huan Zhao, Qian-Wen Wan, Fu-Shan Tang, Department of Clinical Pharmacy, Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, Zunyi 563006, Guizhou Province, China
Author contributions: He XY and Zhao YH contributed equally to this work; He XY and Zhao YH contributed to the manuscript outline and composed the initial draft; He XY and Wan QW were responsible for sourcing and organizing the relevant literature; Tang FS and Zhao YH originated the concept for this manuscript; Tang FS provided supervision, reviewed the paper, and finalized the manuscript; all authors have read and approved the final manuscript.
Conflict-of-interest statement: All the authors have nothing to disclose for this article.
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: Fu-Shan Tang, PhD, Professor, Department of Clinical Pharmacy, Key Laboratory of Basic Pharmacology of Guizhou Province and School of Pharmacy, Zunyi Medical University, No. 6 Xuefu West Road, Xinpu New District, Zunyi 563006, Guizhou Province, China. fstang@vip.163.com
Received: March 18, 2024 Revised: August 22, 2024 Accepted: September 4, 2024 Published online: December 16, 2024 Processing time: 219 Days and 21.6 Hours
Abstract
This editorial discusses an article recently published in the World Journal of Clinical Cases, focusing on risk factors associated with intensive care unit-acquired weakness (ICU-AW). ICU-AW is a serious neuromuscular complication seen in critically ill patients, characterized by muscle dysfunction, weakness, and sensory impairments. Post-discharge, patients may encounter various obstacles impacting their quality of life. The pathogenesis involves intricate changes in muscle and nerve function, potentially leading to significant disabilities. Given its global significance, ICU-AW has become a key research area. The study identified critical risk factors using a multilayer perceptron neural network model, highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW. Recommendations were provided for preventing ICU-AW, emphasizing comprehensive interventions and risk factor mitigation. This editorial stresses the importance of external validation, cross-validation, and model transparency to enhance model reliability. Moreover, the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions. While machine learning presents opportunities, challenges such as model reliability and data management necessitate thorough validation and ethical considerations. In conclusion, integrating machine learning into healthcare offers significant potential and challenges. Enhancing data management, validating models, and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.
Core Tip: This editorial emphasizes the importance of recognizing the risk factors linked to intensive care unit-acquired weakness and highlights the vital role of machine learning in identifying and managing these factors to improve patient outcomes and enhance the quality of care in clinical settings.
