Date of Award

7-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Engineering and Sciences

First Advisor

Ronaldo Menezes

Second Advisor

Nezamoddin Nezamoddini-Kachouie

Third Advisor

William Allen

Fourth Advisor

Eraldo Ribeiro

Abstract

Organ transplantation yearly saves thousands of lives worldwide; yet, 22 people still die each day only in the USA due to the ever-increasing imbalance between the supply and demand of organs. Currently, this organ-allocation gap is mainly tackled by optimally allocating organs based on major survivability factors (i.e., efficiency), by providing the population equal access to transplantation (i.e., equity), and by promoting population health literacy (i.e., awareness). Efficiency, equity, and awareness impact each other; yet, the state-of-the-art in organ transplantation still lacks the characterization of awareness, and the trade-off between these aspects are not fully-understood. Given the current availability of data and computational power, this dissertation proposes a comprehensive data-driven characterization of organ transplantation that accounts for efficiency, equity, and awareness by (i) integrating available data sets; (ii) proposing a sensor for population awareness using social media; and (iii) proposing a novel nonparametric probabilistic data-intensive framework. The dissertation demonstrates that the proposed characterization can uncover patterns of efficiency, equity, and awareness, and has the potential to characterize organ transplantation in a unified manner.

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