Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms

TL;DR

This paper presents a novel machine learning framework that predicts rheumatoid arthritis patients' responses to drugs using electronic health records, improving classification accuracy and aiding clinical decision-making.

Contribution

The study introduces a two-stage ML framework with a complete data processing pipeline and demonstrates improved prediction accuracy over existing models.

Findings

Two-stage DRP outperforms end-to-end models in accuracy

Framework effectively predicts drug response groups

Supports clinical decisions with EHR data

Abstract

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.