Position: Machine Learning for Heart Transplant Allocation Policy Optimization Should Account for Incentives

arXiv:2602.04990v3 Announce Type: replace Abstract: 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-based systems to machine learning and data-driven optimization, we argue that current approaches often overlook a fundamental barrier: incentives. In this position paper, we highlight that organ allocation is not merely an optimization problem, but rather a complex game involving organ procurement organizations, transplant centers, clinicians, patients, and regulators.

Source: arXiv cs.LG — read the full report at the original publisher.

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