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Using machine learning and an ensemble of methods to predict kidney transplant survival

Author

Listed:
  • Ethan Mark
  • David Goldsman
  • Brian Gurbaxani
  • Pinar Keskinocak
  • Joel Sokol

Abstract

We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell’s concordance index, than the Estimated Post Transplant Survival model used in the kidney allocation system in the U.S., and other models published recently in the literature. The model has a five-year concordance index of 0.724 (in comparison, the concordance index is 0.697 for the Estimated Post Transplant Survival model, the state of the art currently in use). It combines predictions from random survival forests with a Cox proportional hazards model. The rankings of importance for the model’s variables differ by transplant recipient age. Better survival predictions could eventually lead to more efficient allocation of kidneys and improve patient outcomes.

Suggested Citation

  • Ethan Mark & David Goldsman & Brian Gurbaxani & Pinar Keskinocak & Joel Sokol, 2019. "Using machine learning and an ensemble of methods to predict kidney transplant survival," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0209068
    DOI: 10.1371/journal.pone.0209068
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    References listed on IDEAS

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    1. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
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    Cited by:

    1. Juliana Feiman Sapiertein Silva & Gustavo Fernandes Ferreira & Marcelo Perosa & Hong Si Nga & Luis Gustavo Modelli de Andrade, 2021. "A machine learning prediction model for waiting time to kidney transplant," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-11, May.
    2. Pinar Keskinocak & Nicos Savva, 2020. "A Review of the Healthcare-Management (Modeling) Literature Published in Manufacturing & Service Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 59-72, January.

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