Machine learning insights on intensive care unit-acquired weakness  

作　　者：Muad Abdi Hassan Abdulqadir J Nashwan 

机构地区：[1]Department of Medical Education,Hamad Medical Corporation,Doha 3050,Qatar [2]Department of Nursing,Hamad Medical Corporation,Doha 3050,Qatar

出　　处：《World Journal of Clinical Cases》2024年第18期3285-3287,共3页世界临床病例杂志

摘　　要：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.

关 键 词：Length of intensive care unit stay Intensive care unit-acquired weakness Machine learning Likelihood factors Precautionary measures 

分 类 号：R619[医药卫生—外科学]