Machine learning insights on intensive care unit-acquired weakness

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.

Keywords: Length of intensive care unit stay; Intensive care unit-acquired weakness; Machine learning; Likelihood factors; Precautionary measures

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 14^th 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.