Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database.

ABSTRACT:

Objective

Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. Our study aims to develop and validate predictive models for the mortality risk.

Methods

This observational study identified patients with SLE requiring hospital admission from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We downloaded data from Fujian Provincial Hospital as an external validation set. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Then, we constructed two predictive models: a traditional nomogram based on logistic regression and a machine learning model employing a stacking ensemble approach. The predictive ability of the mo

SUBMITTER: Chen Z 

PROVIDER: S-EPMC11831314 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature