Graft rejection prediction following kidney transplantation using machine learning techniques: A systematic review and meta-analysis

Aldilas Achmad Nursetyo, Shabbir Syed-Abdul, Mohy Uddin, Yu Chuan Li

研究成果: 書貢獻/報告類型會議貢獻

1 引文 斯高帕斯(Scopus)

摘要

Kidney transplantation is recommended for patients with End-Stage Renal Disease (ESRD). However, complications, such as graft rejection are hard to predict due to donor and recipient variability. This study discusses the role of machine learning (ML) in predicting graft rejection following kidney transplantation, by reviewing the available related literature. PubMed, DBLP, and Scopus databases were searched to identify studies that utilized ML methods, in predicting outcome following kidney transplants. Fourteen studies were included. This study reviewed the deployment of ML in 109,317 kidney transplant patients from 14 studies. We extracted five different ML algorithms from reviewed studies. Decision Tree (DT) algorithms revealed slightly higher performance with overall mean Area Under the Curve (AUC) for DT (79.5% + 0.06) was higher than Artificial Neural Network (ANN) (78.2% + 0.08). For predicting graft rejection, ANN and DT were at the top among ML models that had higher accuracy and AUC.

原文英語
主出版物標題MEDINFO 2019
主出版物子標題Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
編輯Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
發行者IOS Press
頁面10-14
頁數5
ISBN(電子)9781643680026
DOIs
出版狀態已發佈 - 八月 21 2019
事件17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, 法国
持續時間: 八月 25 2019八月 30 2019

出版系列

名字Studies in Health Technology and Informatics
264
ISSN(列印)0926-9630
ISSN(電子)1879-8365

會議

會議17th World Congress on Medical and Health Informatics, MEDINFO 2019
國家法国
城市Lyon
期間8/25/198/30/19

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

指紋 深入研究「Graft rejection prediction following kidney transplantation using machine learning techniques: A systematic review and meta-analysis」主題。共同形成了獨特的指紋。

引用此