Skip to main content
SpringerLink
Log in

Ordinal Classification Approach for Donor-Recipient Matching in Liver Transplantation with Circulatory Death Donors

  • Conference paper
  • First Online:
Advances in Computational Intelligence (IWANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14135))

Included in the following conference series:

  • 388 Accesses

Abstract

This paper tackles the Donor-Recipient (D-R) matching for Liver Transplantation (LT). Typically, D-R matching is performed following the knowledge of a team of experts guided by the use of a prioritisation system. One of the most extended, the Model for End-stage Liver Disease (MELD), aims to decrease the mortality in the waiting list. However, it does not take into account the result of the transplant. In this sense, with the aim of developing a system able to bear in mind the survival benefit, we propose to treat the problem as an ordinal classification one. The organ survival will be predicted at four different thresholds. The results achieved demonstrate that ordinal classifiers are capable of outperforming nominal approaches in the state-of-the-art. Finally, this methodology can help experts make more informed decisions about the appropriateness of assigning a recipient for a specific donor, maximising the probability of post-transplant survival in LT.

This work has been partially subsidised by “Agencia Española de Investigación (España)” (grant ref.: PID2020-115454GB-C22/AEI/10.13039/501100011033). Víctor Manuel Vargas’s research has been subsidised by the FPU Predoctoral Program of the Spanish Ministry of Science, Innovation and Universities (MCIU) (grant reference: FPU18/00358). David Guijo-Rubio’s research has been subsidised by the University of Córdoba through grants to Public Universities for the requalification of the Spanish university system of the Ministry of Universities, financed by the European Union - NextGenerationEU (grant reference: UCOR01MS).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ayllón, M.D., et al.: Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation. Liver Transpl. 24(2), 192–203 (2018)

    Article  PubMed  Google Scholar 

  2. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  Google Scholar 

  3. Briceño, J., Cruz-Ramírez, M., Prieto, M., et al.: Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study. J. Hepatol. 61(5), 1020–1028 (2014)

    Article  PubMed  Google Scholar 

  4. Chang, C.C., Lin, C.J.: LibSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)

    Article  Google Scholar 

  5. Cillo, U., et al.: A multistep, consensus-based approach to organ allocation in liver transplantation: toward a “blended principle model”. Am. J. Transplant. 15(10), 2552–2561 (2015)

    Google Scholar 

  6. Cruz-Ramirez, M., Hervas-Martinez, C., Fernandez, J.C., Briceno, J., De La Mata, M.: Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks. Artif. Intell. Med. 58(1), 37–49 (2013)

    Article  PubMed  Google Scholar 

  7. Cruz-Ramírez, M., Hervás-Martínez, C., Sánchez-Monedero, J., Gutiérrez, P.A.: Metrics to guide a multi-objective evolutionary algorithm for ordinal classification. Neurocomputing 135, 21–31 (2014)

    Article  Google Scholar 

  8. Dutkowski, P., et al.: Are there better guidelines for allocation in liver transplantation?: A novel score targeting justice and utility in the model for end-stage liver disease era. Ann. Surg. 254(5), 745–754 (2011)

    Article  PubMed  Google Scholar 

  9. Frank, E., Hall, M.: A simple approach to ordinal classification. In: De Raedt, L., Flach, P. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 145–156. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44795-4_13

    Chapter  Google Scholar 

  10. Ge, J., Kim, W.R., Lai, J.C., Kwong, A.J.: “beyond meld”–emerging strategies and technologies for improving mortality prediction, organ allocation and outcomes in liver transplantation. J. Hepatol. 76(6), 1318–1329 (2022)

    Google Scholar 

  11. Guijo-Rubio, D., et al.: Ordinal regression algorithms for the analysis of convective situations over Madrid-Barajas airport. Atmos. Res. 236, 104798 (2020)

    Article  Google Scholar 

  12. Gutiérrez, P.A., Perez-Ortiz, M., Sanchez-Monedero, J., Fernandez-Navarro, F., Hervas-Martinez, C.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28(1), 127–146 (2015)

    Article  Google Scholar 

  13. Hessheimer, A.J., et al.: Abdominal normothermic regional perfusion in controlled donation after circulatory determination of death liver transplantation: outcomes and risk factors for graft loss. Am. J. Transplant. 22(4), 1169–1181 (2022)

    Article  PubMed  Google Scholar 

  14. Hinton, G.E.: Connectionist learning procedures. In: Machine Learning, pp. 555–610. Elsevier, Amsterdam (1990)

    Google Scholar 

  15. Kamath, P.S., Kim, W.R.: The model for end-stage liver disease (MELD). Hepatology 45(3), 797–805 (2007)

    Article  PubMed  Google Scholar 

  16. Kong, C., Wang, H., Luo, Q., Mao, R., Chen, G.: Deep multi-input multi-stream ordinal model for age estimation: Based on spatial attention learning. Futur. Gener. Comput. Syst. 140, 173–184 (2023)

    Article  Google Scholar 

  17. Kwon, Y.S., Han, I., Lee, K.C.: Ordinal pairwise partitioning (OPP) approach to neural networks training in bond rating. Intell. Syst. Account. Finance Manag. 6(1), 23–40 (1997)

    Article  Google Scholar 

  18. Lai, Q., et al.: Sarco-model: a score to predict the dropout risk in the perspective of organ allocation in patients awaiting liver transplantation. Liver Int. 41(7), 1629–1640 (2021)

    Article  CAS  PubMed  Google Scholar 

  19. Loh, W.Y.: Classification and regression trees. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 1(1), 14–23 (2011)

    Article  Google Scholar 

  20. Manthoulis, G., Doumpos, M., Zopounidis, C., Galariotis, E.: An ordinal classification framework for bank failure prediction: methodology and empirical evidence for us banks. Eur. J. Oper. Res. 282(2), 786–801 (2020)

    Article  Google Scholar 

  21. McDonald, G.C.: Ridge regression. Wiley Interdisc. Rev. Comput. Stat. 1(1), 93–100 (2009)

    Article  Google Scholar 

  22. Pedregosa, F., Bach, F., Gramfort, A.: On the consistency of ordinal regression methods. J. Mach. Learn. Res. 18(1), 1769–1803 (2017)

    Google Scholar 

  23. Pérez-Ortiz, M., Cruz-Ramírez, M., Ayllón-Terán, M.D., Heaton, N., Ciria, R., Hervás-Martínez, C.: An organ allocation system for liver transplantation based on ordinal regression. Appl. Soft Comput. 14, 88–98 (2014)

    Article  Google Scholar 

  24. Pérez-Ortiz, M., et al.: Synthetic semi-supervised learning in imbalanced domains: constructing a model for donor-recipient matching in liver transplantation. Knowl.-Based Syst. 123, 75–87 (2017)

    Article  Google Scholar 

  25. Pérez-Ortiz, M., Gutiérrez, P.A., Carbonero-Ruz, M., Hervás-Martínez, C.: Semi-supervised learning for ordinal kernel discriminant analysis. Neural Netw. 84, 57–66 (2016)

    Article  PubMed  Google Scholar 

  26. Polyak, A., Kuo, A., Sundaram, V.: Evolution of liver transplant organ allocation policy: current limitations and future directions. World J. Hepatol. 13(8), 830–839 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  27. Rana, A., et al.: Survival outcomes following liver transplantation (soft) score: a novel method to predict patient survival following liver transplantation. Am. J. Transplant. 8(12), 2537–2546 (2008)

    Article  CAS  PubMed  Google Scholar 

  28. Vargas, V.M., et al.: Exponential loss regularisation for encouraging ordinal constraint to shotgun stocks quality assessment. Appl. Soft Comput. 110191 (2023)

    Google Scholar 

  29. Waegeman, W., Boullart, L., et al.: An ensemble of weighted support vector machines for ordinal regression. Int. J. Comput. Syst. Sci. Eng. 3(1), 47–51 (2009)

    Google Scholar 

  30. Wu, H., Lu, H., Ma, S.: A practical SVM-based algorithm for ordinal regression in image retrieval. In: Proceedings of the Eleventh ACM International Conference on Multimedia, pp. 612–621 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Sciences, Universidad de Córdoba, 14014, Córdoba, Spain

    Marcos Rivera-Gavilán, Víctor Manuel Vargas, Pedro Antonio Gutiérrez, César Hervás-Martínez & David Guijo-Rubio

  2. Hospital Universitario Reina Sofía, Córdoba, Spain

    Javier Briceño

  3. School of Computing Sciences, University of East Anglia, Norwich, NR4 7TQ, UK

    David Guijo-Rubio

Authors
  1. Marcos Rivera-Gavilán
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Víctor Manuel Vargas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro Antonio Gutiérrez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javier Briceño
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. César Hervás-Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. David Guijo-Rubio
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Víctor Manuel Vargas .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Málaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Spain

    Andreu Catala

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rivera-Gavilán, M., Vargas, V.M., Gutiérrez, P.A., Briceño, J., Hervás-Martínez, C., Guijo-Rubio, D. (2023). Ordinal Classification Approach for Donor-Recipient Matching in Liver Transplantation with Circulatory Death Donors. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43078-7_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43077-0

  • Online ISBN: 978-3-031-43078-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation