Interpretable Ensemble Machine Learning Prediction of Nonadherence and the Risk of Nonpersistence of Targeted Disease-Modifying Antirheumatic Agents in Older Adults With Rheumatoid Arthritis
Abstract
PURPOSE: Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated interpretable ensemble ML models in evaluating the nonadherence and nonpersistence of biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA). METHODS: This retrospective study used 5% Medicare claims data including older (aged ≥65 years) patients initiating b/tsDMARDs (the index date) between 2013 and 2019, who were diagnosed with RA (the International Classification of Diseases, Ninth and Tenth revision codes). Nonadherence, defined as medication possession ratio
Fields of Study & Subjects
Rheumatoid Arthritis Research and Therapies
Imbalanced Data Classification Techniques
Machine Learning in Healthcare
Publication details
- Journal / source
- Clinical Therapeutics
- Publisher
- Elsevier BV
- Volume
- 48
- Issue
- 3
- Pages
- 214–225
- Publication date
- 21 February 2026
- Language
- EN