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dc.contributor.authorSenanayake, S
dc.contributor.authorBarnett, A
dc.contributor.authorGraves, N
dc.contributor.authorHealy, H
dc.contributor.authorBaboolal, K
dc.contributor.authorKularatna, S
dc.date.accessioned2021-07-05T02:25:32Z
dc.date.available2021-07-05T02:25:32Z
dc.date.issued2020
dc.identifier.issn2046-1402
dc.identifier.doi10.12688/f1000research.20661.2
dc.identifier.urihttp://hdl.handle.net/10072/405651
dc.description.abstractBackground: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients. Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature. However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia. Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models (for live donor and deceased donor transplants). The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherF1000 Research Ltd
dc.relation.ispartofpagefrom1810
dc.relation.ispartofjournalF1000Research
dc.relation.ispartofvolume8
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchOncology and carcinogenesis
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode3202
dc.subject.fieldofresearchcode3211
dc.subject.keywordsGraft failure
dc.subject.keywordsKidney transplant
dc.subject.keywordsMachine learning
dc.subject.keywordsRisk prediction models
dc.titleUsing machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: Protocol for a retrospective cohort study
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationSenanayake, S; Barnett, A; Graves, N; Healy, H; Baboolal, K; Kularatna, S, Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: Protocol for a retrospective cohort study, F1000Research, 2020, 8, pp. 1810
dcterms.dateAccepted2020-03-04
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-07-05T00:47:30Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2020 Senanayake S et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorKularatna, Sanjeewa M.


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