Learning Matching Representations for Individualized Organ Transplantation Allocation
This addresses the critical need for better organ transplantation allocation to improve patient outcomes, representing a domain-specific incremental advance.
The paper tackles the problem of learning data-driven rules for organ donor-recipient matching to improve transplantation success, departing from standard supervised learning by handling two feature spaces and counterfactual matches. Experiments show the proposed model outperforms state-of-the-art allocation methods and human expert policies.
Organ transplantation is often the last resort for treating end-stage illness, but the probability of a successful transplantation depends greatly on compatibility between donors and recipients. Current medical practice relies on coarse rules for donor-recipient matching, but is short of domain knowledge regarding the complex factors underlying organ compatibility. In this paper, we formulate the problem of learning data-driven rules for organ matching using observational data for organ allocations and transplant outcomes. This problem departs from the standard supervised learning setup in that it involves matching the two feature spaces (i.e., donors and recipients), and requires estimating transplant outcomes under counterfactual matches not observed in the data. To address these problems, we propose a model based on representation learning to predict donor-recipient compatibility; our model learns representations that cluster donor features, and applies donor-invariant transformations to recipient features to predict outcomes for a given donor-recipient feature instance. Experiments on semi-synthetic and real-world datasets show that our model outperforms state-of-art allocation methods and policies executed by human experts.