Hassan MA, Nashwan AJ. Machine learning insights on intensive care unit-acquired weakness. World J Clin Cases 2024; 12(18): 3285-3287 [PMID: 38983426 DOI: 10.12998/wjcc.v12.i18.3285]
Corresponding Author of This Article
Abdulqadir J Nashwan, MSc, Research Scientist, Department of Nursing, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa
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. Jun 26, 2024; 12(18): 3285-3287 Published online Jun 26, 2024. doi: 10.12998/wjcc.v12.i18.3285
Machine learning insights on intensive care unit-acquired weakness
Muad Abdi Hassan, Abdulqadir J Nashwan
Muad Abdi Hassan, Department of Medical Education, Hamad Medical Corporation, Doha 3050, Qatar
Abdulqadir J Nashwan, Department of Nursing, Hamad Medical Corporation, Doha 3050, Qatar
Author contributions: Hassan MA and Nashwan AJ contributed to the manuscript's writing, editing, and literature review.
Conflict-of-interest statement: All the authors declare that they have no conflict 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: Abdulqadir J Nashwan, MSc, Research Scientist, Department of Nursing, Hamad Medical Corporation, Rayyan Road, Doha 3050, Qatar. anashwan@hamad.qa
Received: February 22, 2024 Revised: March 14, 2024 Accepted: April 28, 2024 Published online: June 26, 2024 Processing time: 117 Days and 1.8 Hours
Abstract
Intensive care unit-acquired weakness (ICU-AW) significantly hampers patient recovery and increases morbidity. With the absence of established preventive strategies, this study utilizes advanced machine learning methodologies to unearth key predictors of ICU-AW. Employing a sophisticated multilayer perceptron neural network, the research methodically assesses the predictive power for ICU-AW, pinpointing the length of ICU stay and duration of mechanical ventilation as pivotal risk factors. The findings advocate for minimizing these elements as a preventive approach, offering a novel perspective on combating ICU-AW. This research illuminates critical risk factors and lays the groundwork for future explorations into effective prevention and intervention strategies.
Core Tip: The study categorized patients into two groups: Intensive care unit-acquired weakness (ICU-AW) and non-ICU-AW, based on their condition on the 14th day post-ICU admission. The researchers collected data from the initial 14 d of the ICU stay, which included age, comorbidities, sedative and vasopressor dosages, duration of mechanical ventilation, length of the ICU stay, and rehabilitation therapy. They then examined the relationships between these variables and ICU-AW.
