  Organ transplants can improve the life expectancy and quality of life for the
recipient but carries the risk of serious post-operative complications, such as
septic shock and organ rejection. The probability of a successful transplant
depends in a very subtle fashion on compatibility between the donor and the
recipient but current medical practice is short of domain knowledge regarding
the complex nature of recipient-donor compatibility. Hence a data-driven
approach for learning compatibility has the potential for significant
improvements in match quality. This paper proposes a novel system
(ConfidentMatch) that is trained using data from electronic health records.
ConfidentMatch predicts the success of an organ transplant (in terms of the 3
year survival rates) on the basis of clinical and demographic traits of the
donor and recipient. ConfidentMatch captures the heterogeneity of the donor and
recipient traits by optimally dividing the feature space into clusters and
constructing different optimal predictive models to each cluster. The system
controls the complexity of the learned predictive model in a way that allows
for assuring more granular and confident predictions for a larger number of
potential recipient-donor pairs, thereby ensuring that predictions are
"personalized" and tailored to individual characteristics to the finest
possible granularity. Experiments conducted on the UNOS heart transplant
dataset show the superiority of the prognostic value of ConfidentMatch to other
competing benchmarks; ConfidentMatch can provide predictions of success with
95% confidence for 5,489 patients of a total population of 9,620 patients,
which corresponds to 410 more patients than the most competitive benchmark
algorithm (DeepBoost).
