Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases.

ABSTRACT:

Objective

Predict long-term disease worsening and the removal of biosimilar medication in patients with rheumatic diseases.

Methodology

Observational, retrospective descriptive study. Review of a database of patients with immune-mediated inflammatory rheumatic diseases who switched from a biological drug (biosimilar or non-biosimilar) to a biosimilar drug for at least 6 months. We selected the most important variables, from 18 variables, using mutual information tests. As patients with disease worsening are a minority, it is very difficult to make models with conventional machine learning techniques, where the best models would always be trivial. For this reason, we computed different types of imbalanced machine learning models, choosing those with better f1-score and mean R

SUBMITTER: Castro Corredor D 

PROVIDER: S-EPMC10688843 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature