Modelling Survival by Machine Learning Methods in Liver Transplantation: Application to the UNOS Dataset

  • David Guijo-RubioEmail author
  • Pedro J. Villalón-Vaquero
  • Pedro A. Gutiérrez
  • Maria Dolores Ayllón
  • Javier Briceño
  • César Hervás-Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)


The aim of this study is to develop and validate a machine learning (ML) model for predicting survival after liver transplantation based on pre-transplant donor and recipient characteristics. For this purpose, we consider a database from the United Network for Organ Sharing (UNOS), containing 29 variables and 39,095 donor-recipient pairs, describing liver transplantations performed in the United States of America from November 2004 until June 2015. The dataset contains more than a \(74\%\) of censoring, being a challenging and difficult problem. Several methods including proportional-hazards regression models and ML methods such as Gradient Boosting were applied, using 10 donor characteristics, 15 recipient characteristics and 4 shared variables associated with the donor-recipient pair. In order to measure the performance of the seven state-of-the-art methodologies, three different evaluation metrics are used, being the concordance index (ipcw) the most suitable for this problem. The results achieved show that, for each measure, a different technique obtains the highest value, performing almost the same, but, if we focus on ipcw, Gradient Boosting outperforms the rest of the methods.


United Network for Organ Sharing Liver transplant Survival analysis Machine learning 



This research has been partially supported by the Ministerio de Economía, Industria y Competitividad of Spain (Refs. TIN2017-90567-REDT and TIN2017-85887-C2-1-P). D. Guijo-Rubio’s research has been supported by the FPU Predoctoral Program from Spanish Ministry of Education and Science (Grant Ref. FPU16/02128).


  1. 1.
    Kleinbaum, D.G., Klein, M.: Survival Analysis, vol. 3. Springer, New York (2010). Scholar
  2. 2.
    Allison, P.D.: Survival analysis using SAS: a practical guide. SAS Institute (2010)Google Scholar
  3. 3.
    Hong, Z., et al.: Survival analysis of liver transplant patients in Canada 1997–2002. In: Transplantation Proceedings, vol. 38, no. 9, pp. 2951–2956. Elsevier, 2006 NovemberCrossRefGoogle Scholar
  4. 4.
    Abolghasemi, J., Toosi, M.N., Rasouli, M., Taslimi, H.: Survival analysis of liver cirrhosis patients after transplantation using accelerated failure time models. Biomed. Res. Ther. 5(11), 2789–2796 (2018)CrossRefGoogle Scholar
  5. 5.
    Martínez, J.A., et al.: Accuracy of the BAR score in the prediction of survival after liver transplantation. Ann. Hepatol. 18(2), 386–392 (2019)CrossRefGoogle Scholar
  6. 6.
    Wang, P., Li, Y., Reddy, C.K.: Machine learning for survival analysis: a survey. ACM Comput. Surv. (CSUR) 51(6), 110 (2019)CrossRefGoogle Scholar
  7. 7.
    Kiaee, F., Sheikhzadeh, H., Mahabadi, S.E.: Relevance vector machine for survival analysis. IEEE Trans. Neural Netw. Learn. Syst. 27(3), 648–660 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Wang, Z., Wang, C.Y.: Buckley-James boosting for survival analysis with high-dimensional biomarker data. Stat. Appl. Genet. Mol. Biol. 9(1), 1–33 (2010) MathSciNetCrossRefGoogle Scholar
  9. 9.
    Organ Procurement and Transplantation Network. United Network for Organ Sharing database (2019).
  10. 10.
    Harrell, F.E., Califf, R.M., Pryor, D.B., Lee, K.L., Rosati, R.A.: Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15(4), 361–387 (1996)CrossRefGoogle Scholar
  11. 11.
    Uno, H., Cai, T., Pencina, M.J., D’Agostino, R.B., Wei, L.J.: On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat. Med. 30(10), 1105–1117 (2011)MathSciNetGoogle Scholar
  12. 12.
    Lambert, J., Chevret, S.: Summary measure of discrimination in survival models based on cumulative/dynamic time-dependent ROC curves. Stat. Methods Med. Res. 25(5), 2088–2102 (2014) MathSciNetCrossRefGoogle Scholar
  13. 13.
    Simon, N., Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for Cox’s proportional hazards model via coordinate descent. J. Stat. Softw. 39(5), 1–13 (2011)CrossRefGoogle Scholar
  14. 14.
    Cox, D.R.: Regression models and life tables (with discussion). J. R. Stat. Soc. Ser. B 34, 187–220 (1972)zbMATHGoogle Scholar
  15. 15.
    Stute, W.: Consistent estimation under random censorship when covariables are present. J. Multivar. Anal. 45(1), 89–103 (1993)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Friedman, J.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Pölsterl, S., Navab, N., Katouzian, A.: Fast training of support vector machines for survival analysis. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Gama, J., Jorge, A., Soares, C. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9285, pp. 243–259. Springer, Cham (2015). Scholar
  18. 18.
    Chapelle, O., Keerthi, S.S.: Efficient algorithms for ranking with SVMs. Inf. Retr. 13(3), 201–215 (2010)CrossRefGoogle Scholar
  19. 19.
    Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 24 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • David Guijo-Rubio
    • 1
    Email author
  • Pedro J. Villalón-Vaquero
    • 1
  • Pedro A. Gutiérrez
    • 1
  • Maria Dolores Ayllón
    • 2
  • Javier Briceño
    • 2
  • César Hervás-Martínez
    • 1
  1. 1.Department of Computer SciencesUniversidad de CórdobaCórdobaSpain
  2. 2.Unit of Hepatobiliary Surgery and Liver TransplantationCórdobaSpain

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