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English Information

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Authors
# Name
1 Ivar Belizario(ivar@alumni.usp.br)
2 Douglas Teodoro(douglas.teodoro@unige.ch)
3 Luís Andrade(gustavo.modelli@unesp.br)
4 Gabriel Spadon(spadon@dal.ca)
5 José F. Rodrigues-Jr(junio@icmc.usp.br)

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Reference
# Reference
1 Dale, R., Cheng, M., Casselman Pines, K., & Currie, M. (2024). Inconsistent values and algorithmic fairness: A review of organ allocation priority systems in the United States. BMC Medical Ethics, 25.
2 Deshpande, R. (2024). Smart match: Revolutionizing organ allocation through artificial intelligence. Frontiers in Artificial Intelligence, 7:1364149.
3 Ding, S., Zha, D., Zhang, K., Chen, L., Jiang, X., Hu, X., & Zou, N. (2025). FairAlloc: Learning fair organ allocation policy for liver transplant. Journal of Healthcare Informatics Research.
4 Elalouf, A., & Pliskin, J. S. (2022). Balancing equity and efficiency in kidney allocation: An overview. Cambridge Quarterly of Healthcare Ethics, 31(3), 321–332.
5 Jalilvand, N., Bairamzadeh, S., Tavakkoli-Moghaddam, R., & Azaron, A. (2023). A bi-objective organ transplant supply chain network with recipient priority considering carbon emission under uncertainty. Computers & Industrial Engineering, 181, 109295.
6 Kupiec-Weglinski, J. W. (2022). Grand challenges in organ transplantation. Frontiers in Transplantation, 1, 897679.
7 Li, H., Zhang, W., & Chen, L. (2023). Predicting kidney transplant outcomes using machine learning: A systematic review. Frontiers in Medicine, 10, 1158704.
8 Lima, B. A., Reis, F., Alves, H., & Henriques, T. S. (2023). Equity matrix for kidney transplant allocation. Transplant Immunology, 81, 101917.
9 Matas, A. J., Smith, D. L., Skeans, J. A., & Stewart, J. P. (2023). OPTN/SRTR 2022 Annual Data Report: Kidney. American Journal of Transplantation, 23(S1), 1–49.
10 Naqvi, S. A. A., Tennankore, K., Worthen, G., Vinson, A., & Abidi, S. S. R. (2025). A reinforcement learning framework for optimizing kidney allocation for transplant based on survival and ethical criteria. In Artificial Intelligence in Medicine (pp. 323–332). Springer Nature.
11 Okano, C. d. S., Menezes, C. C. S. d., Brandão, F. A., Carletto, V. R., & de Souza, M. A. (2023). Analysis of the national transplant scenario in Brazil. Research, Society and Development, 12(9).
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13 Salaün, A., Knight, S., Wingfield, L., & Zhu, T. (2024). Predicting graft and patient outcomes following kidney transplantation using interpretable machine learning models. Scientific Reports, 14(1), 17356.
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15 Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
16 Taherkhani, N., Sepehri, M. M., Shafaghi, S., & Khatibi, T. (2019). Identification and weighting of kidney allocation criteria: A novel multi-expert fuzzy method. BMC Medical Informatics and Decision Making, 19(1), 182.
17 Tang, C., Abbatematteo, B., Hu, J., Chandra, R., Martín-Martín, R., & Stone, P. (2025). Deep reinforcement learning for robotics: A survey of real-world successes. Annual Review of Control, Robotics, and Autonomous Systems, 8, 153–188.
18 Tonelli, M., Wiebe, N., Knoll, G., et al. (2011). Kidney transplantation compared with dialysis in clinically relevant outcomes: A systematic review. The Lancet, 378(9800), 180–189.
19 Zhao, D., Huanshi, X., & Zhang, X. (2024). Active exploration deep reinforcement learning for continuous action space with forward prediction. International Journal of Computational Intelligence Systems, 17.
20 Zhu, Y., et al. (2025). Deep reinforcement learning of mobile robot navigation: A comparative analysis of value-based, policy-based and hybrid methods. Sensors, 25(11).