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).
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References
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)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
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)
Chang, C.C., Lin, C.J.: LibSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)
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)
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)
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)
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)
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
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)
Guijo-Rubio, D., et al.: Ordinal regression algorithms for the analysis of convective situations over Madrid-Barajas airport. Atmos. Res. 236, 104798 (2020)
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)
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)
Hinton, G.E.: Connectionist learning procedures. In: Machine Learning, pp. 555–610. Elsevier, Amsterdam (1990)
Kamath, P.S., Kim, W.R.: The model for end-stage liver disease (MELD). Hepatology 45(3), 797–805 (2007)
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)
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)
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)
Loh, W.Y.: Classification and regression trees. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 1(1), 14–23 (2011)
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)
McDonald, G.C.: Ridge regression. Wiley Interdisc. Rev. Comput. Stat. 1(1), 93–100 (2009)
Pedregosa, F., Bach, F., Gramfort, A.: On the consistency of ordinal regression methods. J. Mach. Learn. Res. 18(1), 1769–1803 (2017)
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)
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)
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)
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)
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)
Vargas, V.M., et al.: Exponential loss regularisation for encouraging ordinal constraint to shotgun stocks quality assessment. Appl. Soft Comput. 110191 (2023)
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)
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)
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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
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