Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics.

ABSTRACT:

Background

We developed a model to predict remissions in patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) and to identify important clinical features associated with remission using explainable artificial intelligence (XAI).

Methods

We gathered the follow-up data of 1204 patients treated with bDMARDs (etanercept, adalimumab, golimumab, infliximab, abatacept, and tocilizumab) from the Korean College of Rheumatology Biologics and Targeted Therapy Registry. Remission was predicted at 1-year follow-up using baseline clinical data obtained at the time of enrollment. Machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost) were used for the predictions. The Shapley additive explanation (SHAP) value was used fo

SUBMITTER: Koo BS 

PROVIDER: S-EPMC8259419 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature