Salehnasab Cirruse, Hajifathali Abbas, Asadi Farkhondeh, Roshandel Elham, Kazemi Alireza, Roshanpoor Arash
Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Hematopoietic Stem Cell Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Methods Inf Med. 2019 Dec;58(6):205-212. doi: 10.1055/s-0040-1709150. Epub 2020 Apr 29.
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.
This article aims to synthesize the literature on ML classification algorithms for predicting aGvHD, highlighting algorithms and important predictor variables used.
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 algorithms in the process of predicting of aGvHD were considered.
After applying the inclusion and exclusion criteria, 14 studies were selected for evaluation. The results of the current analysis showed that the algorithms used were Artificial Neural Network (79%), Support Vector Machine (50%), Naive Bayes (43%), k-Nearest Neighbors (29%), Regression (29%), and Decision Trees (14%), respectively. Also, many predictor variables have been used in these studies so that we have divided them into more abstract categories, including biomarkers, demographics, infections, clinical, genes, transplants, drugs, and other variables.
Each of these ML algorithms has a particular characteristic and different proposed predictors. Therefore, it seems these ML algorithms have a high potential for predicting aGvHD if the process of modeling is performed correctly.
急性移植物抗宿主病（aGvHD）是接受异基因造血干细胞移植患者死亡的最重要原因。鉴于其发生在严重组织损伤阶段，诊断较晚。随着机器学习（ML）的发展，出现了有前景的预测aGvHD的实时模型。
本文旨在综合关于预测aGvHD的ML分类算法的文献，突出所使用的算法和重要预测变量。
根据系统评价和Meta分析的首选报告项目（PRISMA）声明，对截至2019年4月在PubMed、Embase、科学网、Scopus、Springer和IEEE Xplore数据库中进行的用于预测aGvHD的ML分类算法的系统评价进行了检索。考虑了专注于在预测aGvHD过程中使用ML分类算法的研究。
应用纳入和排除标准后，选择了14项研究进行评估。当前分析结果表明，所使用的算法分别为人工神经网络（79%）、支持向量机（50%）、朴素贝叶斯（43%）、k近邻（29%）、回归（29%）和决策树（14%）。此外，这些研究中使用了许多预测变量，因此我们将它们分为更抽象的类别，包括生物标志物、人口统计学、感染、临床、基因、移植、药物和其他变量。
这些ML算法中的每一种都有其特定特征和不同的预测变量。因此，如果建模过程正确执行，这些ML算法似乎在预测aGvHD方面具有很高的潜力。
