{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T14:36:35Z","timestamp":1774276595819,"version":"3.50.1"},"update-to":[{"DOI":"10.12688\/f1000research.20661.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T00:00:00Z","timestamp":1583712000000}}],"reference-count":49,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T00:00:00Z","timestamp":1583712000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001793","name":"Queensland University of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001793","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003521","name":"Department of Education, Australian Governement","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003521","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns4:p>\n                    <ns4:bold>Background:<\/ns4:bold>\n                    A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.\u00a0 Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature.\u00a0 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.\u00a0\u00a0\n                  <\/ns4:p>\n                  <ns4:p>\n                    <ns4:bold>Methods:<\/ns4:bold>\n                    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\n                    <ns4:sup>st<\/ns4:sup>\n                    , 2007, to December 31\n                    <ns4:sup>st<\/ns4:sup>\n                    , 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\u2019s performance.\n                  <\/ns4:p>\n                  <ns4:p>\n                    <ns4:bold>Discussion:<\/ns4:bold>\n                    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.\n                  <\/ns4:p>","DOI":"10.12688\/f1000research.20661.2","type":"journal-article","created":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T08:35:10Z","timestamp":1583742910000},"page":"1810","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study"],"prefix":"10.12688","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5606-2046","authenticated-orcid":false,"given":"Sameera","family":"Senanayake","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6339-0374","authenticated-orcid":false,"given":"Adrian","family":"Barnett","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5559-3267","authenticated-orcid":false,"given":"Nicholas","family":"Graves","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helen","family":"Healy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keshwar","family":"Baboolal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjeewa","family":"Kularatna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2560","published-online":{"date-parts":[[2020,3,9]]},"reference":[{"key":"ref-1","doi-asserted-by":"publisher","DOI":"10.2337\/db18-1618-P","article-title":"Chronic Kidney Disease (CKD) in U.S. Adults with Self-Reported Cardiovascular Disease (CVD)\u2014A National Estimate of Prevalence by KDIGO 2012 Classification.","volume":"67","author":"T Wang","year":"2018","journal-title":"Am Diabetes Assoc."},{"key":"ref-2","doi-asserted-by":"publisher","first-page":"e158-e65","DOI":"10.1097\/CCM.0000000000002878","article-title":"Hospital Variation in Renal Replacement Therapy for Sepsis in the United States.","volume":"46","author":"T Valley","year":"2018","journal-title":"Crit Care Med."},{"key":"ref-3","doi-asserted-by":"publisher","first-page":"997-9","DOI":"10.1056\/NEJMp058318","article-title":"Chronic kidney disease in the developing world.","volume":"354","author":"R Barsoum","year":"2006","journal-title":"N Engl J Med."},{"key":"ref-4","doi-asserted-by":"publisher","first-page":"561-9","DOI":"10.1159\/000345552","article-title":"Bayesian modeling of pretransplant variables accurately predicts kidney graft survival.","volume":"36","author":"T Brown","year":"2012","journal-title":"Am J Nephrol."},{"key":"ref-5","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s12911-015-0206-y","article-title":"Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods.","volume":"15","author":"A Decruyenaere","year":"2015","journal-title":"BMC Med Inform Decis mak."},{"key":"ref-6","first-page":"259-64","article-title":"Risk factors for prolonged hospitalization after kidney transplants.","volume":"11","author":"A Matas","year":"1997","journal-title":"Clin Transplant."},{"key":"ref-7","first-page":"1115-8","article-title":"The high cost of delayed graft function in cadaveric renal transplantation.","volume":"51","author":"J Rosenthal","year":"1991","journal-title":"Transplantation."},{"key":"ref-8","doi-asserted-by":"publisher","first-page":"1401-6","DOI":"10.1097\/TP.0b013e31818b221f","article-title":"Prediction of graft survival of living-donor kidney transplantation: nomograms or artificial neural networks?","volume":"86","author":"A Akl","year":"2008","journal-title":"Transplantation."},{"key":"ref-9","doi-asserted-by":"publisher","first-page":"1134-6","DOI":"10.1016\/j.transproceed.2010.03.061","article-title":"Decisional trees in renal transplant follow-up.","volume":"42","author":"R Greco","year":"2010","journal-title":"Transplant Proc."},{"key":"ref-10","doi-asserted-by":"publisher","first-page":"944-52","DOI":"10.1016\/j.jbi.2008.03.005","article-title":"Single and multiple time-point prediction models in kidney transplant outcomes.","volume":"41","author":"R Lin","year":"2008","journal-title":"J Biomed Inform."},{"key":"ref-11","doi-asserted-by":"publisher","first-page":"744-51","DOI":"10.1053\/j.ajkd.2010.12.017","article-title":"Development and evaluation of a composite risk score to predict kidney transplant failure.","volume":"57","author":"J Moore","year":"2011","journal-title":"Am J Kidney Dis."},{"key":"ref-12","doi-asserted-by":"publisher","first-page":"1288-94","DOI":"10.1038\/ki.2010.232","article-title":"A clinical scoring system highly predictive of long-term kidney graft survival.","volume":"78","author":"Y Foucher","year":"2010","journal-title":"Kidney Int."},{"key":"ref-13","doi-asserted-by":"publisher","first-page":"1248-55","DOI":"10.1016\/j.juro.2008.10.164","article-title":"Nomograms for predicting graft function and survival in living donor kidney transplantation based on the UNOS Registry.","volume":"181","author":"H Tiong","year":"2009","journal-title":"J Urol."},{"key":"ref-14","doi-asserted-by":"publisher","first-page":"231-6","DOI":"10.1097\/TP.0b013e3181ac620b","article-title":"A comprehensive risk quantification score for deceased donor kidneys: the kidney donor risk index.","volume":"88","author":"P 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Methodol."},{"key":"ref-21","doi-asserted-by":"publisher","first-page":"8904","DOI":"10.1038\/s41598-017-08008-8","article-title":"A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study.","volume":"7","author":"K Yoo","year":"2017","journal-title":"Sci Rep."},{"key":"ref-22","article-title":"Data fields for the study.docx.","author":"S Senanayake","year":"2020","journal-title":"figshare."},{"key":"ref-23","first-page":"1065-9","article-title":"Tree-structured survival analysis.","volume":"69","author":"L Gordon","year":"1985","journal-title":"Cancer Treat Rep."},{"key":"ref-24","doi-asserted-by":"publisher","first-page":"841-60","DOI":"10.1214\/08-AOAS169","article-title":"Random survival forests","volume":"2","author":"H Ishwaran","year":"2008","journal-title":"Ann Appl Stat."},{"key":"ref-25","doi-asserted-by":"publisher","first-page":"412-423","DOI":"10.32614\/rj-2018-005","article-title":"Support Vector Machines for Survival Analysis with R.","volume":"10","author":"C Fouodo","year":"2018","journal-title":"R J."},{"key":"ref-26","first-page":"209-17","article-title":"Application of z-score transformation to Affymetrix data.","volume":"2","author":"C Cheadle","year":"2003","journal-title":"Appl Bioinformatics."},{"key":"ref-27","doi-asserted-by":"publisher","first-page":"558-69","DOI":"10.4097\/kja.19087","article-title":"Multicollinearity and misleading statistical results.","volume":"72","author":"J Kim","year":"2019","journal-title":"Korean J Anesthesiol."},{"key":"ref-28","doi-asserted-by":"publisher","first-page":"37-52","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis.","volume":"2","author":"S Wold","year":"1987","journal-title":"Chemometr Intell Lab Syst."},{"key":"ref-29","doi-asserted-by":"publisher","first-page":"301-20","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net.","volume":"67","author":"H Zou","year":"2005","journal-title":"J R Stat Soc Series B Stat Methodol."},{"key":"ref-30","doi-asserted-by":"crossref","first-page":"267-88","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso.","volume":"58","author":"R Tibshirani","year":"1996","journal-title":"J R Statist Soc B."},{"key":"ref-31","doi-asserted-by":"publisher","first-page":"51-57","DOI":"10.1111\/bju.14477","article-title":"Use of machine learning to predict early biochemical recurrence after robot-assisted prostatectomy.","volume":"123","author":"N Wong","year":"2019","journal-title":"BJU Int."},{"key":"ref-32","article-title":"R: A language and environment for statistical computing","year":"2013"},{"key":"ref-33","first-page":"2002","article-title":"Cox proportional-hazards regression for survival data","author":"J Fox","year":"2002"},{"key":"ref-34","doi-asserted-by":"crossref","DOI":"10.1201\/9780429246593","article-title":"An introduction to the bootstrap","author":"B Efron","year":"1994"},{"key":"ref-35","doi-asserted-by":"publisher","first-page":"123-40","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors.","volume":"24","author":"L Breiman","year":"1996","journal-title":"Mach Learn."},{"key":"ref-36","doi-asserted-by":"publisher","first-page":"445-63","DOI":"10.1023\/a:1016409317640","article-title":"Decision trees: an overview and their use in medicine.","volume":"26","author":"V Podgorelec","year":"2002","journal-title":"J Med Syst."},{"key":"ref-37","doi-asserted-by":"publisher","first-page":"603-9","DOI":"10.1016\/s0895-4356(00)00344-9","article-title":"The use of classification and regression trees in clinical epidemiology.","volume":"54","author":"R Marshall","year":"2001","journal-title":"J Clin Epidemol."},{"key":"ref-38","doi-asserted-by":"publisher","first-page":"456-462","DOI":"10.1016\/j.bspc.2017.01.012","article-title":"Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation.","volume":"52","author":"T Shaikhina","year":"2019","journal-title":"Biomedical Signal Processing and Control."},{"key":"ref-39","doi-asserted-by":"publisher","first-page":"296-305","DOI":"10.1002\/jmri.22432","article-title":"Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma.","volume":"33","author":"X Hu","year":"2011","journal-title":"J Magn Reson Imaging."},{"key":"ref-40","doi-asserted-by":"publisher","first-page":"901-912","DOI":"10.1007\/s11517-018-1930-0","article-title":"A reliable method for colorectal cancer prediction based on feature selection and support vector machine.","volume":"57","author":"D Zhao","year":"2019","journal-title":"Med Biol Eng Comput."},{"key":"ref-41","article-title":"On ranking in survival analysis: Bounds on the concordance index","author":"H Steck","year":"2008"},{"key":"ref-42","doi-asserted-by":"publisher","first-page":"46-9","DOI":"10.1016\/j.trre.2013.01.003","article-title":"Australian registries-ANZDATA and ANZOD.","volume":"27","author":"S McDonald","year":"2013","journal-title":"Transplant Rev (Orlando)."},{"key":"ref-43","doi-asserted-by":"publisher","first-page":"2654-62","DOI":"10.1056\/NEJMoa031643","article-title":"Long-term survival of children with end-stage renal disease.","volume":"350","author":"S McDonald","year":"2004","journal-title":"N Engl J Med."},{"key":"ref-44","doi-asserted-by":"publisher","first-page":"1482-8","DOI":"10.1097\/TP.0b013e3181dd35a0","article-title":"Open and laparoscopic donor nephrectomy: activity and outcomes from all Australasian transplant centers.","volume":"89","author":"N Brook","year":"2010","journal-title":"Transplantation."},{"key":"ref-45","doi-asserted-by":"publisher","first-page":"399-405","DOI":"10.1111\/j.1600-6143.2012.04312.x","article-title":"Inferior early posttransplant outcomes for recipients of right versus left deceased donor kidneys: an ANZDATA registry analysis.","volume":"13","author":"H Vacher-Coponat","year":"2013","journal-title":"Am J Transplant."},{"key":"ref-46","doi-asserted-by":"publisher","first-page":"133-44","DOI":"10.1016\/j.asej.2014.09.007","article-title":"Extracting software static defect models using data mining.","volume":"6","author":"A Yousef","year":"2015","journal-title":"Ain Shams Engineering Journal."},{"key":"ref-47","article-title":"How Complex is your classification problem? A survey on measuring classification complexity.","author":"A Lorena","year":"2018","journal-title":"arXiv preprint."},{"key":"ref-48","doi-asserted-by":"publisher","first-page":"515-20.e2","DOI":"10.1016\/j.jtcvs.2013.10.011","article-title":"Clinical, biochemical, and genetic predictors of coronary artery bypass graft failure.","volume":"148","author":"B Yanagawa","year":"2014","journal-title":"J Thorac Cardiovasc Surg."},{"key":"ref-49","doi-asserted-by":"publisher","first-page":"29-38","DOI":"10.1055\/s-0035-1563605","article-title":"Recipient Criteria Predictive of Graft Failure in Kidney Transplantation.","volume":"25","author":"E Molmenti","year":"2016","journal-title":"Int J Angiol."}],"container-title":["F1000Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/f1000research.com\/articles\/8-1810\/v2\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/8-1810\/v2\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/8-1810\/v2\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T17:13:44Z","timestamp":1722532424000},"score":1,"resource":{"primary":{"URL":"https:\/\/f1000research.com\/articles\/8-1810\/v2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,9]]},"references-count":49,"URL":"https:\/\/doi.org\/10.12688\/f1000research.20661.2","relation":{"has-review":[{"id-type":"doi","id":"10.5256\/f1000research.25046.r62013","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.25046.r61037","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.25046.r62016","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.25046.r61037","asserted-by":"object"},{"id-type":"doi","id":"10.5256\/f1000research.25046.r62013","asserted-by":"object"},{"id-type":"doi","id":"10.5256\/f1000research.25046.r62016","asserted-by":"object"}]},"ISSN":["2046-1402"],"issn-type":[{"value":"2046-1402","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,9]]},"assertion":[{"value":"Indexed","URL":"https:\/\/f1000research.com\/articles\/8-1810\/v2#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.25046.r61037, Ramkiran Gouripeddi, Department of Biomedical Informatics, Center for Clinical and Translational Sciences (CCTS) Biomedical Informatics Core, University of Utah, Salt Lake City, UT, USA, 14 Apr 2020, version 2, indexed","URL":"https:\/\/f1000research.com\/articles\/8-1810\/v2#referee-response-61037","order":0,"name":"referee-response-61037","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.25046.r62013, Chenxi Huang, Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA, 21 Apr 2020, version 2, indexed","URL":"https:\/\/f1000research.com\/articles\/8-1810\/v2#referee-response-62013","order":1,"name":"referee-response-62013","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.25046.r62016, Slade Matthews, Pharmacoinformatics Laboratory, Discipline of Pharmacology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia, 04 May 2020, version 2, indexed","URL":"https:\/\/f1000research.com\/articles\/8-1810\/v2#referee-response-62016","order":2,"name":"referee-response-62016","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"Sameera Senanayake is a recipient of Australian Government Research Training Program (RTP) for Postgraduate Research (PhD) Scholarship and Queensland University of Technology International Postgraduate Research (PhD) Scholarship (2018 -2021). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":3,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Data associated with the article are available under the terms of the Creative Commons Zero \"No rights reserved\" data waiver (CC0 1.0 Public domain dedication).","order":0,"name":"copyright-info","label":"Copyright"}]}}