Optimal Kidney Exchange with Immunosuppressants


  • Haris Aziz UNSW Sydney Data61 CSIRO
  • Ágnes Cseh Hasso Plattner Institute, University of Potsdam Institute of Economics, Centre for Economic and Regional Studies
  • John P. Dickerson University of Maryland
  • Duncan C. McElfresh University of Maryland


Healthcare, Medicine & Wellness


Algorithms for exchange of kidneys is one of the key successful applications in market design, artificial intelligence, and operations research. Potent immunosuppressant drugs suppress the body's ability to reject a transplanted organ up to the point that a transplant across blood- or tissue-type incompatibility becomes possible. In contrast to the standard kidney exchange problem, we consider a setting that also involves the decision about which recipients receive from the limited supply of immunosuppressants that make them compatible with originally incompatible kidneys. We firstly present a general computational framework to model this problem. Our main contribution is a range of efficient algorithms that provide flexibility in terms of meeting meaningful objectives. Motivated by the current reality of kidney exchanges using sophisticated mathematical-programming-based clearing algorithms, we then present a general but scalable approach to optimal clearing with immunosuppression; we validate our approach on realistic data from a large fielded exchange.




How to Cite

Aziz, H., Cseh, Ágnes, Dickerson, J. P., & McElfresh, D. C. (2021). Optimal Kidney Exchange with Immunosuppressants. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 21-29. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16073



AAAI Technical Track on Application Domains