A systematic review of the predicted outcomes related to hematopoietic stem cell transplantation: focus on applied machine learning methods’ performance

Abstract

ABSTRACT Introduction Hematopoietic stem cell transplantation (HSCT) is a critical therapeutic procedure in blood diseases, and the investigation of HSCT data can provide valuable information. Machine learning (ML) techniques are useful data analysis tools which applied in many studies to predict HSCT survival and estimate the risk of transplantation. Areas covered A systematic review was performed with a search of PubMed, Science Direct, Embase, Scopus, and the European Society for Blood and Marrow Transplantation, the Center for International Blood and Marrow Transplant Research, and the American Society for Transplantation and Cellular Therapy publications for articles published by September 2020. Expert opinion 24 papers that met eligibility criteria were included in this study. The applied ML algorithms with the highest performance were Random Survival Forests (AUC = 0.72) for survival-related, Random Survival Forests and Logistic Regression (AUC = 0.77) for mortality-related, Deep Learning (AUC = 0.8) for relapse, L2-Regularized Logistic Regression (AUC = 0.66) for Acute-Graft Versus Host Disease, Random Survival Forests (AUC = 0.88) for sepsis, Elastic-Net Regression (AUC = 0.89) for cognitive impairment, and Bayesian Network (AUC = 0.997) for oral mucositis outcome. This review reveals the potential of ML techniques to predict HSCT outcomes and apply them to developing clinical decision support systems.