Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study [version 2; peer review: 2 approved, 1 approved with reservations]
Background: 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,...
Main Authors: | Sameera Senanayake, Adrian Barnett, Nicholas Graves, Helen Healy, Keshwar Baboolal, Sanjeewa Kularatna |
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Format: | Article |
Language: | English |
Published: |
F1000 Research Ltd
2020-03-01
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Series: | F1000Research |
Online Access: | https://f1000research.com/articles/8-1810/v2 |
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