Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis
Jeroen Berrevoets,u00a0Ahmed Alaa,u00a0Zhaozhi Qian,u00a0James Jordon,u00a0Alexander E. S. Gimson,u00a0Mihaela van der Schaar
Organ transplantation is often the last resort for treating end-stage illnesses, but managing transplant wait-lists is challenging because of organ scarcity and the complexity of assessing donor-recipient compatibility. In this paper, we develop a data-driven model for (real-time) organ allocation using observational data for transplant outcomes. Our model integrates a queuing-theoretic framework with unsupervised learning to cluster the organs into u201corgan typesu201d, and then construct priority queues (associated with each organ type) wherein incoming patients are assigned. To reason about organ allocations, the model uses synthetic controls to infer a patientu2019s survival outcomes under counterfactual allocations to the different organ types{u2013} the model is trained end-to-end to optimise the trade-off between patient waiting time and expected survival time. The usage of synthetic controls enable patient-level interpretations of allocation decisions that can be presented and understood by clinicians. We test our model on multiple data sets, and show that it outperforms other organ-allocation policies in terms of added life-years, and death count. Furthermore, we introduce a novel organ-allocation simulator to accurately test new policies.
