Adaptively Weighted Top-N Recommendation for Organ Matching

TL;DR

This paper introduces an adaptively weighted top-N recommendation method for organ matching, improving decision accuracy by incorporating actual post-transplantation performance data, thus enhancing organ allocation efficiency.

Contribution

It proposes a novel AWTR method that leverages historical matching outcomes and covariates to optimize top-N organ-patient recommendations, addressing limitations of traditional scoring models.

Findings

AWTR outperforms seven benchmark methods in simulation studies.

The method emphasizes top-N ranking accuracy over overall recommendation accuracy.

Simulation results demonstrate improved matching performance with AWTR.

Abstract

Reducing the shortage of organ donations to meet the demands of patients on the waiting list has being a major challenge in organ transplantation. Because of the shortage, organ matching decision is the most critical decision to assign the limited viable organs to the most suitable patients. Currently, organ matching decisions were only made by matching scores calculated via scoring models, which are built by the first principles. However, these models may disagree with the actual post-transplantation matching performance (e.g., patient's post-transplant quality of life (QoL) or graft failure measurements). In this paper, we formulate the organ matching decision-making as a top-N recommendation problem and propose an Adaptively Weighted Top-N Recommendation (AWTR) method. AWTR improves performance of the current scoring models by using limited actual matching performance in historical data set as well as the collected covariates from organ donors and patients. AWTR sacrifices the overall recommendation accuracy by emphasizing the recommendation and ranking accuracy for top-N matched patients. The proposed method is validated in a simulation study, where KAS [60] is used to simulate the organ-patient recommendation response. The results show that our proposed method outperforms seven state-of-the-art top-N recommendation benchmark methods.