Predicting EQ-5D full health state in systemic lupus erythematosus using machine learning algorithms.

ABSTRACT:

Objectives

To determine factors associated with reports of EuroQol 5-Dimensions (EQ-5D) full health state (FHS) before and after a trial intervention in patients with SLE, resorting to machine learning algorithms.

Methods

We conducted a post hoc analysis of two phase 3 clinical trials of belimumab (BLISS-52, BLISS-76). Demographic, laboratory and clinical features were retrieved and the Monte Carlo Feature Selection algorithm was employed, then further refined upon consideration of collinearity and clinical relevance. We used support vector machine with radial basis function kernel (SVMRadial), least absolute shrinkage and selection operator (LASSO), neural network (NNet) and logistic regression (LR) to capture both linear and non-linear relationships while ensuring interpre

SUBMITTER: Botto J 

PROVIDER: S-EPMC12007597 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature