Posters and Spotlights
Machine Learning for Predicting Treatment Response to Biologic and Targeted Synthetic Disease Modifying Antirheumatic Drugs (b/tsDMARDs) in Rheumatoid Arthritis: A Scoping Review
Start Date
30-4-2025 11:30 AM
Document Type
Event
Description
Poster Presenter: E. Eriakha
Research Team: Eriakha E; Yu Han; Mai Li; Jieni Li; Yinan Huang
Abstract: Rheumatoid arthritis (RA) contributes to significant patient morbidity, disability, and reduced quality of life globally and in the United States (U.S.) Despite significant advancements in treatment options, treatment responses among patients with RA vary widely. Machine learning (ML) methods offer opportunities for personalized predictions, potentially improving treatment outcomes. However, current evidence on ML use for predicting RA treatment responses remains heterogeneous and unclear. This study aimed to systematically summarize current evidence on the application of ML methods in predicting treatment responses to b/tsDMARDs among patients with RA.
Relational Format
poster
Recommended Citation
Eriakha, E., "Machine Learning for Predicting Treatment Response to Biologic and Targeted Synthetic Disease Modifying Antirheumatic Drugs (b/tsDMARDs) in Rheumatoid Arthritis: A Scoping Review" (2025). Showcase of Research and Scholarly Activity. 13.
https://egrove.olemiss.edu/ored_showcase/2025/posters/13
Machine Learning for Predicting Treatment Response to Biologic and Targeted Synthetic Disease Modifying Antirheumatic Drugs (b/tsDMARDs) in Rheumatoid Arthritis: A Scoping Review
Poster Presenter: E. Eriakha
Research Team: Eriakha E; Yu Han; Mai Li; Jieni Li; Yinan Huang
Abstract: Rheumatoid arthritis (RA) contributes to significant patient morbidity, disability, and reduced quality of life globally and in the United States (U.S.) Despite significant advancements in treatment options, treatment responses among patients with RA vary widely. Machine learning (ML) methods offer opportunities for personalized predictions, potentially improving treatment outcomes. However, current evidence on ML use for predicting RA treatment responses remains heterogeneous and unclear. This study aimed to systematically summarize current evidence on the application of ML methods in predicting treatment responses to b/tsDMARDs among patients with RA.