Optimizing Hard-to-Place Kidney Allocation: A Machine Learning Approach to Center Ranking

• URL: http://arxiv.org/abs/2410.09116v1
• Date: Thu, 10 Oct 2024 17:15:41 GMT
• Title: Optimizing Hard-to-Place Kidney Allocation: A Machine Learning Approach to Center Ranking
• Authors: Sean Berry, Berk Gorgulu, Sait Tunc, Mucahit Cevik, Matthew J Ellis,
• Abstract summary: This paper proposes a data-driven, machine learning-based ranking system for allocating hard-to-place kidneys to centers. Our experiments demonstrate that the proposed policy can reduce the average number of centers considered before placement by fourfold for all kidneys and tenfold for hard-to-place kidneys.
• Score: 0.4893345190925178
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Kidney transplantation is the preferred treatment for end-stage renal disease, yet the scarcity of donors and inefficiencies in allocation systems create major bottlenecks, resulting in prolonged wait times and alarming mortality rates. Despite their severe scarcity, timely and effective interventions to prevent non-utilization of life-saving organs remain inadequate. Expedited out-of-sequence placement of hard-to-place kidneys to centers with the highest likelihood of utilizing them has been recommended in the literature as an effective strategy to improve placement success. Nevertheless, current attempts towards this practice is non-standardized and heavily rely on the subjective judgment of the decision-makers. This paper proposes a novel data-driven, machine learning-based ranking system for allocating hard-to-place kidneys to centers with a higher likelihood of accepting and successfully transplanting them. Using the national deceased donor kidney offer and transplant datasets, we construct a unique dataset with donor-, center-, and patient-specific features. We propose a data-driven out-of-sequence placement policy that utilizes machine learning models to predict the acceptance probability of a given kidney by a set of transplant centers, ranking them accordingly based on their likelihood of acceptance. Our experiments demonstrate that the proposed policy can reduce the average number of centers considered before placement by fourfold for all kidneys and tenfold for hard-to-place kidneys. This significant reduction indicates that our method can improve the utilization of hard-to-place kidneys and accelerate their acceptance, ultimately reducing patient mortality and the risk of graft failure. Further, we utilize machine learning interpretability tools to provide insights into factors influencing the kidney allocation decisions.

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