Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study.
Journal:
European journal of medical research
Published Date:
Jul 24, 2025
Abstract
BACKGROUND: Intensive care unit (ICU)-acquired weakness (ICUAW) is a prevalent complication in critically ill patients, marked by symmetrical respiratory and limb muscle weakness, which adversely affects long-term outcomes. Early identification of high-risk patients and prevention are essential to mitigate its impact. Traditional risk prediction models, based on cohort data, have limitations in addressing the complex, non-linear relationships among diverse risk factors due to patient heterogeneity and the dynamic nature of critical illness. Machine learning offers a promising alternative by integrating heterogeneous data-clinical, laboratory, and physiological-to enhance predictive accuracy and individualization. Additionally, machine learning can identify novel risk factors and mechanisms overlooked by conventional methods, supporting early intervention and targeted prevention strategies to improve patient prognosis. Therefore, this study aims to develop and validate risk prediction models for ICUAW based on multiple machine learning algorithms.
