4 papers
Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives
Ioannis Anagnostides, Itai Zilberstein, Zachary W. Sollie +2
The allocation of scarce donor organs constitutes one of the most consequential algorithmic challenges in healthcare. While the field is rapidly transitioning from rigid, rule-base…
Learning Potentials for Dynamic Matching and Application to Heart Transplantation
Itai Zilberstein, Ioannis Anagnostides, Zachary W. Sollie +2
Each year, thousands of patients in need of heart transplants face life-threatening wait times due to organ scarcity. While allocation policies aim to maximize population-level out…
Near-Optimal Dynamic Matching via Coarsening with Application to Heart Transplantation
Itai Zilberstein, Ioannis Anagnostides, Zachary W. Sollie +2
Online matching has been a mainstay in domains such as Internet advertising and organ allocation, but practical algorithms often lack strong theoretical guarantees. We take an impo…
Policy Optimization for Dynamic Heart Transplant Allocation
Ioannis Anagnostides, Zachary W. Sollie, Arman Kilic +1
Heart transplantation is a viable path for patients suffering from advanced heart failure, but this lifesaving option is severely limited due to donor shortage. Although the curren…