Date on Master's Thesis/Doctoral Dissertation
5-2025
Document Type
Doctoral Dissertation
Degree Name
Ph. D.
Department
Industrial Engineering
Degree Program
Industrial Engineering, PhD
Committee Chair
Gentili, Monica
Committee Member
Chen, Xiaoyu
Committee Member
Saleem, Jason
Committee Member
Aqlan, Faisal
Committee Member
Waterman, Amy
Author's Keywords
living kidney donation; organ trafficking; social network analysis; artificial intelligence; healthcare optimization; prompt engineering
Abstract
Kidney transplantation is the gold standard for treating end-stage renal disease, yet over 90,000 patients remain on the transplant waitlist. This dissertation introduces engineering-driven solutions to help reduce the transplant supply gap by addressing three challenges: illicit trafficking, donor recruitment, and evaluation inefficiencies. First, we model illicit organ trafficking networks using social network analysis. We demonstrate that targeting transplant clinics alone is insufficient to disrupt operations. Instead, disrupting the network requires detaining brokers who organize behind-the-scenes logistics—offering a more effective strategy for intervention. Second, we seek to identify a latent population of potential living donors who face barriers such as limited information, health concerns, or financial worries. Using deep learning models on online discussions and expert-informed schema design, we classify potential donor profiles and the factors influencing their decisions. To improve model accuracy and adaptability, we introduce Combinatorial Promptimization, a novel automatic prompt engineering technique that outperforms Google DeepMind’s PromptBreeder on the GSM8K benchmark. Third, we address inefficiencies in the donor evaluation process, where 8–86% of potential donors drop out. We propose an optimization model for the Single-Dependence Sequential Testing Problem, minimizing the total expected cost and time required for medical testing. Together, these methods show how tools from network science, AI, and operations research can strengthen legitimate transplant systems, support potential donors, and accelerate evaluations—helping to increase the number of living kidney donations.
Recommended Citation
Nielsen, Joshua, "Engineering solutions for the transplant supply gap: social network analysis, artificial intelligence, and optimization in living kidney donation." (2025). Electronic Theses and Dissertations. Paper 4581.
Retrieved from https://ir.library.louisville.edu/etd/4581
