Optimal Kidney Exchange with Immunosuppressants

Authors

  • 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

Keywords:

Healthcare, Medicine & Wellness

Abstract

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.

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Published

2021-05-18

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

Issue

Section

AAAI Technical Track on Application Domains