| 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).
|
|
| 12 |
Sá, G. C. B. e., & Madeira, C. A. G. (2025). Deep reinforcement learning in real-time strategy games: A systematic literature review. Applied Intelligence, 55(3), 243.
|
|
| 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.
|
|
| 14 |
Salomão Pontes, D. F., Fernandes Ferreira, G., Segev, D., Massie, A. B., Levan, M., Barbosa, A. M. P., da Rocha, N. C., & Modelli de Andrade, L. G. (2024). Regional disparities in kidney transplant allocation in Brazil: A retrospective cohort study. Clinical Transplantation, 38(9), e15446.
|
|
| 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).
|
|