A Machine Learning Approach Using Survival Statistics to Predict GraftSurvival in Kidney Transplant Recipients: A Multicenter Cohort Study

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/473706.do
DOI
10.1038/s41598-017-08008-8
Title
A Machine Learning Approach Using Survival Statistics to Predict GraftSurvival in Kidney Transplant Recipients: A Multicenter Cohort Study
Description
This work was supported by the Interdisciplinary Research InitiativesProgram from College of Engineering and College of Medicine, SeoulNational University (grant no. 800-20150095).
abstract
Accurate prediction of graft survival after kidney transplant is limited by the complexity and heterogeneity of risk factors influencing allograft survival. In this study, we applied machine learning methods, in combination with survival statistics, to build new prediction models of graft survival that included immunological factors, as well as known recipient and donor variables. Graft survival was estimated from a retrospective analysis of the data from a multicenter cohort of 3,117 kidney transplant recipients. We evaluated the predictive power of ensemble learning algorithms (survival decision tree, bagging, random forest, and ridge and lasso) and compared outcomes to those of conventional models (decision tree and Cox regression). Using a conventional decision tree model, the 3-month serum creatinine level post-transplant (cut-off, 1.65 mg/dl) predicted a graft failure rate of 77.8% (index of concordance, 0.71). Using a survival decision tree model increased the index of concordance to 0.80, with the episode of acute rejection during the first year post-transplant being associated with a 4.27-fold increase in the risk of graft failure. Our study revealed that early acute rejection in the first year is associated with a substantially increased risk of graft failure. Machine learning methods may provide versatile and feasible tools for forecasting graft survival.
provenance
Made available in Cube on 2018-09-28T10:26:43Z (GMT). No. of bitstreams: 0
language
English
author
Yoo, Kyung Don
Noh, Junhyug
Lee, Hajeong
Kim, Dong Ki
Lim, Chun Soo
Kim, Young Hoon
Lee, Jung Pyo
Kim, Gunhee
Kim, Yon Su
orcid
Kim, Dong Ki/0000-0002-5195-7852
accessioned
2018-09-28T10:26:43Z
available
2018-09-28T10:26:43Z
issued
2017
citation
SCIENTIFIC REPORTS(7)
issn
2045-2322
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/473706.do
Funder
교육부
Funding Program
BK21플러스사업(0.5)
Project ID
1345274075
Jurisdiction
Rep.of Korea
Project Name
SNU BK21 Plus Program for Pioneers in Innovative Computing
rights
openAccess
type
article


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