Single and multiple time-point prediction models in kidney transplant outcomes
- 31 December 2008
- journal article
- research article
- Published by Elsevier BV in Journal of Biomedical Informatics
- Vol. 41 (6), 944-952
- https://doi.org/10.1016/j.jbi.2008.03.005
Abstract
No abstract availableKeywords
This publication has 19 references indexed in Scilit:
- Similar risk profiles for post-transplant renal dysfunction and long-term graft failure: UNOS/OPTN database analysisKidney International, 2004
- Prediction of delayed renal allograft function using an artificial neural networkNephrology Dialysis Transplantation, 2003
- Prediction of 3‐yr cadaveric graft survival based on pre‐transplant variables in a large national datasetClinical Transplantation, 2003
- Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effectsStatistics in Medicine, 2002
- Modeling Medical Prognosis: Survival Analysis TechniquesJournal of Biomedical Informatics, 2001
- A neural network approach to the biopsy diagnosis of early acute renal transplant rejectionHistopathology, 1999
- Proportional hazards tests and diagnostics based on weighted residualsBiometrika, 1994
- A scaled conjugate gradient algorithm for fast supervised learningNeural Networks, 1993
- Empirical comparisons of proportional hazards and logistic regression modelsStatistics in Medicine, 1990
- Nonparametric Estimation from Incomplete ObservationsJournal of the American Statistical Association, 1958