Interpretable Ensemble Machine Learning Prediction of Nonadherence and the Risk of Nonpersistence of Targeted Disease-Modifying Antirheumatic Agents in Older Adults With Rheumatoid Arthritis

Q: What does this research mean for the field?

Machine learning models can effectively predict nonadherence and nonpersistence of targeted disease-modifying antirheumatic agents in older adults with rheumatoid arthritis. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

Yinan Huang; Sandeep Agarwal

doi:10.1016/j.clinthera.2026.01.007

February 1, 2026Clinical TherapeuticsOpen Access

Interpretable Ensemble Machine Learning Prediction of Nonadherence and the Risk of Nonpersistence of Targeted Disease-Modifying Antirheumatic Agents in Older Adults With Rheumatoid Arthritis

View Full Paper

Ask AI

Bookmark

Share

Key Points

The aim was to develop machine learning models to predict nonadherence and nonpersistence of b/tsDMARDs in older adults with rheumatoid arthritis.
Developed machine learning models
Focused on older adults with rheumatoid arthritis
Analyzed patterns of nonadherence and nonpersistence
Identified key factors influencing nonadherence
Predicted risk of nonpersistence with high accuracy
Demonstrated potential for personalized treatment approaches

Ask about this paper

Answers are grounded in this paper and cite their sources.

Implication

Machine learning predicts nonadherence and nonpersistence of treatment in older adults with rheumatoid arthritis, suggesting advancements in personalized medicine.

Abstract

We developed ML models predicting nonadherence and nonpersistence of b/tsDMARDs for older adults with RA. There is potential to leverage ML for understanding patients' behavior of utilizing b/tsDMARDs treatment with a goal towards personalized medicine in RA.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Huang et al. (2026) studied this question.

synapsesocial.com/papers/699d3f9ede8e28729cf64374 https://doi.org/10.1016/j.clinthera.2026.01.007

View Full Paper

Ask AI

Bookmark

Share

Interpretable Ensemble Machine Learning Prediction of Nonadherence and the Risk of Nonpersistence of Targeted Disease-Modifying Antirheumatic Agents in Older Adults With Rheumatoid Arthritis

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Interpretable Ensemble Machine Learning Prediction of Nonadherence and the Risk of Nonpersistence of Targeted Disease-Modifying Antirheumatic Agents in Older Adults With Rheumatoid Arthritis

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading