Date of Award:
8-2025
Document Type:
Dissertation
Degree Name:
Doctor of Philosophy (PhD)
Department:
Mathematics and Statistics
Committee Chair(s)
Kevin Moon (committee chair) Christopher Corcoran (committee co-chair)
Committee
Kevin Moon
Committee
Christopher Corcoran
Committee
Yan Sun
Committee
John Stevens
Committee
Rakesh Kaundal
Abstract
Healthcare generates vast amounts of data daily, from genetic profiles to hospital records, but much of it remains untapped due to its complexity. This dissertation develops new computational tools to unlock this data’s potential, aiming to improve patient care and medical research. Five projects tackle different challenges: Project 1 creates Deep MAGIC, a method to fill in missing genetic and image data accurately, vital for understanding diseases like cancer. Project 2 analyzes how the COVID-19 pandemic disrupted surgeries, finding a 27% drop and temporary complication rises in 2020, guiding future crisis planning. Projects 3 and 4 study kidney disease trials, confirming reliable shortcuts to test treatments faster and cheaper. Project 5 introduces SIREN, predicting health outcomes with limited data, useful for early disease detection. Together, these tools turn raw data into practical solutions, paving the way for personalized medicine and better healthcare decisions.
Checksum
9e836a58f92de25fc9640adac6ee80f5
Recommended Citation
Eddington, Devin P., "Unveiling Insights From Complexity: Advanced Computational Techniques for High-Dimensional Medical Data" (2025). All Graduate Theses and Dissertations, Fall 2023 to Present. 602.
https://digitalcommons.usu.edu/etd2023/602
Included in
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .