  Rheumatoid arthritis (RA) is an autoimmune condition caused when patients'
immune system mistakenly targets their own tissue. Machine learning (ML) has
the potential to identify patterns in patient electronic health records (EHR)
to forecast the best clinical treatment to improve patient outcomes. This study
introduced a Drug Response Prediction (DRP) framework with two main goals: 1)
design a data processing pipeline to extract information from tabular clinical
data, and then preprocess it for functional use, and 2) predict RA patient's
responses to drugs and evaluate classification models' performance. We propose
a novel two-stage ML framework based on European Alliance of Associations for
Rheumatology (EULAR) criteria cutoffs to model drug effectiveness. Our model
Stacked-Ensemble DRP was developed and cross-validated using data from 425 RA
patients. The evaluation used a subset of 124 patients (30%) from the same data
source. In the evaluation of the test set, two-stage DRP leads to improved
classification accuracy over other end-to-end classification models for binary
classification. Our proposed method provides a complete pipeline to predict
disease activity scores and identify the group that does not respond well to
anti-TNF treatments, thus showing promise in supporting clinical decisions
based on EHR information.
