Abstract

Background The acute graft-versus-host disease (aGvHD) is the most important cause of mortality in patients receiving allogeneic hematopoietic stem cell transplantation. Given that it occurs at the stage of severe tissue damage, its diagnosis is late. With the advancement of machine learning (ML), promising real-time models to predict aGvHD have emerged.
Objective This article aims to synthesize the literature on ML classification algorithms for predicting aGvHD, highlighting algorithms and important predictor variables used.
Methods A systemic review of ML classification algorithms used to predict aGvHD was performed using a search of the PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases undertaken up to April 2019 based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statements. The studies with a focus on using the ML classification …

Date

2019

Authors

Cirruse Salehnasab, Abbas Hajifathali, Farkhondeh Asadi, Elham Roshandel, Alireza Kazemi, Arash Roshanpoor

Source

Methods of Information in Medicine

Volume

58

Issue

06

Pages

205-212

Publisher

Georg Thieme Verlag KG