A decision-rule for transplanting non-cadaveric organs

Probability Model of Decision Making for Successful Transplantation of Non-Cadaveric Organs (RESEARCH NOTE)

Mathematical modeling based on a probabilistic approach for making decisions for organ transplantation can be successfully employed in cases when the choice of decisions can affect the results produced. In this study, the minimum probability of success required for organ transplantion in case of multi-donors is determined. The governing equations are constructed in terms of probabilities and so...

Do the sick have a right to cadaveric organs?

One way of increasing the supply of organs for transplantation is to adopt a policy giving the sick a right to cadaveric organs. Such a right would entail the coercive transfer of organs from the dead without their previous consent. Because this policy would violate individual autonomy and the special relation between humans and their bodies, it would be morally unjustifiable. Although a rights...

MMDT: Multi-Objective Memetic Rule Learning from Decision Tree

In this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. This...

OPTIMAL POLICIES FOR THE ACCEPTANCE OF LIVING- AND CADAVERIC-DONOR LIVERS by

Transplantation is the only viable therapy for end-stage liver diseases (ESLD) such as hepatitis B. In the United States, patients with ESLD are placed on a waiting list. When organs become available, they are offered to the patients on this waiting list. This dissertation fo-cuses on the decision problem faced by these patients: which offer to accept and which to refuse? This decision depends ...

Market and non-market mechanisms for procuring human and cadaveric organs: when the price is right.