Using supervised machine learning approach to predict treatment outcomes of vedolizumab in ulcerative colitis patients
Jingjing Chen, Manon Girard, Song Wang, Krisztina Kisfalvi, Richard A. Lirio
Abstract
With recent advances in machine learning, we demonstrated the use of supervised machine learning to optimize the prediction of treatment outcomes of vedolizumab through iterative optimization using VARSITY and VISIBLE 1 data in patients with moderate-to-severe ulcerative colitis. The analysis was carried out using elastic net regularized regression following a 2-stage training process. The model performance was assessed through AUROC, specificity, sensitivity, and accuracy. The generalizable predictive patterns suggest that easily obtained baseline and medical history variables may be able to predict therapeutic response to vedolizumab with clinically meaningful accuracy, implying a potential for individualized prescription of vedolizumab.