Advancing critical care recovery:The pivotal role of machine learning in early detection of intensive care unit-acquired weakness  

作　　者：Georges Khattar Elie Bou Sanayeh 

机构地区：[1]Department of Medicine,Staten Island University Hospital,Staten Island,NY 10305,United States

出　　处：《World Journal of Clinical Cases》2024年第21期4455-4459,共5页世界临床病例杂志

摘　　要：This editorial explores the significant challenge of intensive care unit-acquiredweakness(ICU-AW),a prevalent condition affecting critically ill patients,characterizedby profound muscle weakness and complicating patient recovery.Highlightingthe paradox of modern medical advances,it emphasizes the urgent needfor early identification and intervention to mitigate ICU-AW's impact.Innovatively,the study by Wang et al is showcased for employing a multilayer perceptronneural network model,achieving high accuracy in predicting ICU-AWrisk.This advancement underscores the potential of neural network models inenhancing patient care but also calls for continued research to address limitationsand improve model applicability.The editorial advocates for the developmentand validation of sophisticated predictive tools,aiming for personalized carestrategies to reduce ICU-AW incidence and severity,ultimately improving patientoutcomes in critical care settings.

关 键 词：Critical illness myopathy Critical illness polyneuropathy Early detection Intensive care unit-acquired weakness Neural network models Patient outcomes Personalized intervention strategies Predictive modeling 

分 类 号：R459.7[医药卫生—急诊医学]