Predictive modeling of ICU-AW inflammatory factors based on machine learning.

ABSTRACT:

Background

ICU-acquired weakness (ICU-AW) is a common complication among ICU patients. We used machine learning techniques to construct an ICU-AW inflammatory factor prediction model to predict the risk of disease development and reduce the incidence of ICU-AW.

Methods

The least absolute shrinkage and selection operator (LASSO) technique was used to screen key variables related to ICU-AW. Eleven indicators, such as the presence of sepsis, glucocorticoids (GC), neuromuscular blocking agents (NBAs), length of ICU stay, Acute Physiology and Chronic Health Evaluation (APACHE II) II score, and the levels of albumin (ALB), lactate (LAC), glucose (GLU), interleukin-1β (IL-1β), interleukin-6 (IL-6), and interleukin-10 (IL-10), were used as variables to establish the prediction model

SUBMITTER: Guo Y 

PROVIDER: S-EPMC11658462 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature