Ordinal classification approach for donor-recipient matching in liver transplantation with circulatory death donors

Research areas:

• Uncategorized

Year:

2023

Type of Publication:

In Proceedings

Keywords:

Donor-Recipient Matching, Liver Transplantation, Ordinal Classification, Ordinal Binary Decomposition

Authors:

• Rivera-Gavilán, M.
• Vargas, Víctor Manuel
• Gutiérrez, Pedro Antonio
• Briceño, J.
• Hervás-Martínez, César
• Guijo-Rubio, David

Volume:

14135

Book title:

IWANN 2023: Advances in Computational Intelligence

Series:

Lecture Notes in Computer Science (LNCS)

Pages:

517–528

Organization:

Ponta Delgada, Portugal

Month:

19th-21th June, 2023

BibTex:

@conference{OrdinalClassification_DR_2023, author = "M. Rivera-Gavil{\'a}n and V{\'i}ctor Manuel Vargas and Pedro Antonio Guti{\'e}rrez and J. Brice{\~n}o and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Guijo-Rubio", 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 to make more informed decisions about the appropriateness of assigning a recipient for a specific donor, maximising the probability of post-transplant survival in LT.", booktitle = "IWANN 2023: Advances in Computational Intelligence", doi = "10.1007/978-3-031-43078-7_42", keywords = "Donor-Recipient Matching, Liver Transplantation, Ordinal Classification, Ordinal Binary Decomposition", month = "19th-21th June, 2023", organization = "Ponta Delgada, Portugal", pages = "517–528", publisher = "Springer", series = "Lecture Notes in Computer Science (LNCS)", title = "{O}rdinal classification approach for donor-recipient matching in liver transplantation with circulatory death donors", url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_42", volume = "14135", year = "2023", }

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 to make more informed decisions about the appropriateness of assigning a recipient for a specific donor, maximising the probability of post-transplant survival in LT.