Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis

Jeroen Berrevoets, Ahmed Alaa, Zhaozhi Qian, James Jordon, Alexander E. S. Gimson, Mihaela van der Schaar
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:792-802, 2021.

Abstract

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 “organ types”, 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 patient’s survival outcomes under counterfactual allocations to the different organ types{–} 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-berrevoets21a, title = {Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis}, author = {Berrevoets, Jeroen and Alaa, Ahmed and Qian, Zhaozhi and Jordon, James and Gimson, Alexander E. S. and van der Schaar, Mihaela}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {792--802}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/berrevoets21a/berrevoets21a.pdf}, url = {https://proceedings.mlr.press/v139/berrevoets21a.html}, abstract = {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 “organ types”, 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 patient’s survival outcomes under counterfactual allocations to the different organ types{–} 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.} }
Endnote
%0 Conference Paper %T Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis %A Jeroen Berrevoets %A Ahmed Alaa %A Zhaozhi Qian %A James Jordon %A Alexander E. S. Gimson %A Mihaela van der Schaar %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-berrevoets21a %I PMLR %P 792--802 %U https://proceedings.mlr.press/v139/berrevoets21a.html %V 139 %X 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 “organ types”, 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 patient’s survival outcomes under counterfactual allocations to the different organ types{–} 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.
APA
Berrevoets, J., Alaa, A., Qian, Z., Jordon, J., Gimson, A.E.S. & van der Schaar, M.. (2021). Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:792-802 Available from https://proceedings.mlr.press/v139/berrevoets21a.html.

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