Optimizing clinical prediction model for new-onset atrial fibrillation in critically ill patient: Based on machine learning.

ABSTRACT:

Background

New-onset atrial fibrillation (NOAF) increases the risk of embolism and sudden death in critically ill patients; however, limited data exist attempting to identify modifiable risk factors and predict the incidence of NOAF. We aimed to investigate the risk factors for NOAF and develop an optimized clinical prediction model based on machine learning algorithms.

Materials and methods

Data from patients admitted to the intensive care unit (ICU) of the Affiliated Hospital of Nanjing University of Chinese Medicine from August 2019 to January 2022 were retrospectively analyzed. LASSO regression and Random Forest (RF) algorithms were used to screen predictive variables. Logistic Regression, RF, Gradient Boosting and Support Vector Machine models were constructed to evalua

SUBMITTER: Wang DC 

PROVIDER: S-EPMC12425216 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature