Predicting outcomes of renal transplant recipients using standard statistical techniques is difficult. Novel approaches such as the use of artificial neural networks might improve the precision and accuracy in this area of medicine in which numerous and complex events contribute to outcomes.
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Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index
BMC Medical Research Methodology Open Access 21 June 2021
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References
Schold, J. D. & Howard, R. J. Prediction models assessing transplant center performance: can a little knowledge be a dangerous thing? Am. J. Transplant. 6, 245–246 (2006).
Kaplan, B., Schold, J. & Meier-Kriesche, H. U. Poor predictive value of serum creatinine for renal allograft loss. Am. J. Transplant. 3, 1560–1565 (2003).
Akl, A., Ismail, A. M. & Ghoneim, M. Prediction of graft survival of living-donor kidney transplantation: nomograms or artificial neural networks? Transplantation 86, 1401–1406 (2008).
Wolfe, R. A. et al. Calculating life years from transplant (LYFT): methods for kidney and kidney–pancreas candidates. Am. J. Transplant. 8, 997–1011 (2008).
Kattan, M. W. Comparison of Cox regression with other methods for determining prediction models and nomograms. J. Urol. 170 (Suppl.), S6–S9 (2003).
Shariat, S. F., Karakiewicz, P. I., Suardi, N. & Kattan, M. W. Comparison of nomograms with other methods for predicting outcomes in prostate cancer: a critical analysis of the literature. Clin. Cancer Res. 14, 4400–4407 (2008).
Meier-Kriesche, H. U., Schold, J. D., Srinivas, T. R. & Kaplan, B. Lack of improvement in renal allograft survival despite a marked decrease in acute rejection rates over the most recent era. Am. J. Transplant. 4, 378–383 (2004).
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Kaplan, B., Schold, J. Neural networks for predicting graft survival. Nat Rev Nephrol 5, 190–192 (2009). https://doi.org/10.1038/nrneph.2009.24
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DOI: https://doi.org/10.1038/nrneph.2009.24
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