Machine Learning Classification Algorithms to Predict aGvHD following Allo-HSCT: A Systematic Review.
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),...
| Publicado en: | Methods of Information in Medicine Vol. 58; no. 6; pp. 205 - 213 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Thieme Medical Publishing Inc.
2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142975824&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142975824 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00261270 W7M jtl: Methods of Information in Medicine issn: 00261270 maglogo: N pubinfo: dt: 2019 vid: 58 iid: 6 pid: 2811 pub: Thieme Medical Publishing Inc. place: New York, New York artinfo: ui: 142975824 142975824 NLM32349154 10.1055/s-0040-1709150 NLM32349154 142975824 ppf: 205 ppct: 8 formats: tig: atl: Machine Learning Classification Algorithms to Predict aGvHD following Allo-HSCT: A Systematic Review. aug: au: Salehnasab, Cirruse Hajifathali, Abbas Asadi, Farkhondeh Roshandel, Elham Kazemi, Alireza Roshanpoor, Arash affil: Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran sug: subj: Hematopoietic Stem Cell Transplantation Adverse Effects Graft Versus Host Disease Etiology Graft Versus Host Disease Diagnosis Algorithms Allografts Information Retrieval Arthritis Impact Measurement Scales Scales ab: 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 algorithms in the process of predicting of aGvHD were considered.Results: 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.Conclusion: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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