Supplementary Material for: Machine learning for the prediction of survival post-allogeneic hematopoietic cell transplantation: A single-center experience

Introduction: Prediction of outcomes following allogeneic hematopoietic cell transplantation (HCT) remains a major challenge. Machine learning (ML) is a computational procedure that may facilitate the generation of HCT prediction models. We sought to investigate the prognostic potential of multiple ML algorithms when applied to a large single-center allogeneic HCT database. Methods: Our registry included 2697 patients that underwent allogeneic HCT from January 1976 to December 2017, 45 pre-transplant baseline variables were included in the predictive assessment of each ML algorithm on overall survival (OS) as determined by area under the curve (AUC). Pre-transplant variables used in the EBMT machine learning study (Shouval et al, 2015) were used as a benchmark for comparison. Results: On the entire dataset, the random forest (RF) algorithm performed best (AUC 0.71±0.04) compared to the second-best model, logistic regression (LR) (AUC=0.69±0.04) (p