A Data Driven Framework to Identify the Critical Variables, Visualize Their Conditional Relations and Predict the Outcomes of U.S. Heart Transplants

Abstract

Predicting the survival of heart transplant patients is an important, yet challenging problem since it plays a crucial role in understanding the matching procedure between a donor and a recipient. Recent studies have shown that data mining models can be used to effectively analyze and extract novel information from large/complex transplantation datasets. The objective of this dissertation is to gain hidden, novel and useful information from these large and complex heart transplant datasets by employing data mining techniques, which helps decision makers to have a better understanding. Specifically, this work: 1) identifies the predictive factors for short-, mid- and long-term survival after the heart transplant, as well as their time-dependent effects on the given follow-up time point. Therefore, it enables us to differentiate the factors whose effect change over time, 2) develops a DSS tool that provides the patient-specific failure risk score based on the values of the relevant preoperative predictors, as well as to investigate the conditional relations among the important predictors of long term survival after heart transplants and 3) is an exploratory study that is still in progress, which evaluates the effect of the newly added variables to the predictability of the survival outcome. Overall, the research goal is to develop mathematical models and tools that present important retrospective findings, which can be the basis for a prospective medical studies.