Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Apr 26, 2024; 12(12): 2023-2030
Published online Apr 26, 2024. doi: 10.12998/wjcc.v12.i12.2023
Predicting intensive care unit-acquired weakness: A multilayer perceptron neural network approach
Carlos Martin Ardila, Daniel González-Arroyave, Mateo Zuluaga-Gómez
Carlos Martin Ardila, Department of Basic Sciences, University of Antioquia, Medellín 52-59, Colombia
Daniel González-Arroyave, Department of Surgery, Pontificia Universidad Bolivariana, Medellín 0057, Colombia
Mateo Zuluaga-Gómez, Department of Emergency, Universidad Pontificia Bolivariana, Medellín 0057, Colombia
Author contributions: Ardila CM performed the conceptualization, data curation, data analysis, manuscript writing, and revision of the manuscript; González-Arroyave D performed the data analysis, manuscript writing, and revision of the manuscript; Zuluaga-Gómez M performed data analysis, manuscript writing, and revision 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: Carlos Martin Ardila, DDS, PhD, Doctor, Postdoc, Professor, Science Editor, Department of Basic Sciences, Biomedical Stomatology Research Group, University of Antioquia, Medellín 52-59, Colombia. martin.ardila@udea.edu.co
Received: February 23, 2024
Peer-review started: February 23, 2024
First decision: March 9, 2024
Revised: March 9, 2024
Accepted: March 22, 2024
Article in press: March 22, 2024
Published online: April 26, 2024

In this editorial, we comment on the article by Wang and Long, published in a recent issue of the World Journal of Clinical Cases. The article addresses the challenge of predicting intensive care unit-acquired weakness (ICUAW), a neuromuscular disorder affecting critically ill patients, by employing a novel processing strategy based on repeated machine learning. The editorial presents a dataset comprising clinical, demographic, and laboratory variables from intensive care unit (ICU) patients and employs a multilayer perceptron neural network model to predict ICUAW. The authors also performed a feature importance analysis to identify the most relevant risk factors for ICUAW. This editorial contributes to the growing body of literature on predictive modeling in critical care, offering insights into the potential of machine learning approaches to improve patient outcomes and guide clinical decision-making in the ICU setting.

Keywords: Intensive care units, Intensive care unit-acquired weakness, Risk factors, Machine learning, Computer neural network

Core Tip: Predicting intensive care unit-acquired weakness (ICUAW) is crucial for improving patient outcomes. This editorial presents the potential of machine learning, specifically the multilayer perceptron neural network model, in predicting ICUAW. Insights into ICUAW risk factors and guides clinical decision-making in critical care are offered. The importance of developing accurate and reliable predictive models to improve patient outcomes in the intensive care unit setting is also emphasized.