Prediction of Persistence to Treatment for Patients with Rheumatoid Arthritis using Deep Learning

Sammanfattning: Rheumatoid Arthritis is an inflammatory joint disease that is one of the most common autoimmune diseases in the world. The treatment usually starts with a first-line treatment called Methotrexate, but it is often insufficient. One of the most common second-line treatments is Tumor Necrosis Factor inhibitors (TNFi). Although some patients respond to TNFi, it has a risk of side effects, including infections. Hence, ability to predict patient responses to TNFi becomes important to choose the correct treatment. This work presents a new approach to predict if the patients were still on TNFi, 1 year after they started, by using a generative neural network architecture called Variational Autoencoder (VAE). We combined a VAE and a classifier neural network to create a supervised learning model called Supervised VAE (SVAE), trained on two versions of a tabular dataset containing Swedish register data. The datasets consist of 7341 patient records, and our SVAE achieved an AUROC score of 0.615 on validation data. Nevertheless, compared to machine learning models previously used for the same prediction task, SVAE achieved higher scores than decision trees and elastic net but lower scores than random forest and gradient-boosted decision tree. Despite the regularization effect that VAEs provide during classification training, the scores achieved by the SVAEs tested during this thesis were lower than the acceptable discrimination level.