Doctoral Dissertations
Date of Award
8-2024
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Industrial Engineering
Major Professor
Bing Yao
Committee Members
Xueping Li, Anahita Khojandi, Lan Gao
Abstract
The rapid advancement in sensing and information technology has ushered us into an era of data explosion, where a large amount of data is now easily available and accessible in the clinical environment. This wealth of healthcare data offers new avenues for developing automated data-driven methods for disease diagnosis. Electronic Health Records (EHRs), serving as digital repositories of a patient's medical information, present unique opportunities to analyze and decipher clinical events and patterns within large populations. Given the rich information about a patient's health trajectory, leveraging EHRs through data-driven methodologies can significantly enhance clinical decision support systems.
However, utilizing real-world EHRs for reliable data-driven disease detection presents several challenges due to the observational nature of EHRs. Unlike well-defined, randomized longitudinal experiments, EHRs are recorded only when patients receive care, resulting in complex and highly heterogeneous data. This dissertation focuses on developing robust and reliable advanced machine-learning algorithms to investigate complex EHR data for disease prediction, addressing the challenges of diverse and complex data types, incomplete datasets, and imbalanced class distributions in EHRs.
Recommended Citation
Wang, Zekai, "Advanced Machine Learning for Data-driven Disease Prediction. " PhD diss., University of Tennessee, 2024.
https://trace.tennessee.edu/utk_graddiss/10524