  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.
