Generative digital twins for longitudinal simulation and augmentation of multi-modal patient data
Year: 2025
Type: dissertation
Abstract: Generative modeling and digital twin technologies have emerged as transformative approaches to addressing critical challenges in healthcare, particularly regarding data availability, privacy, completeness, and quality.While machine learning has demonstrated significant potential in areas such as patient outcome prediction, drug discovery, and clinical trial optimization, its widespread application in real-world clinical settings remains constrained by the inherent limitations of medical data.These limitations include fragmented data repositories, modality-specific gaps, missing data, and stringent privacy requirements, all of which restrict data sharing and integration.My PhD research addresses these fundamental challenges by developing innovative generative digital twin frameworks designed to robustly simulate, repair, augment, and enhance multi-modal patient data.This research comprises several key methodological contributions: (i) Generation of privatized synthetic electronic health records (EHRs) to facilitate data sharing without compromising data quality or realism; (ii) Enhancement of existing EHR datasets to address inherent biases and quality concerns through advanced generative modeling; (iii) Generative augmentation techniques specifically tailored for medical imaging to repair and complete datasets via conditional simulation, improving downstream analytical performance; and (iv) Creation of universal, modality-agnostic representations of medical imaging data that enable robust model development despite heterogeneous and limited datasets.Collectively, these generative digital twin approaches significantly improve the usability and integrity of real-world multi-modal healthcare data.By overcoming critical barriers related to data incompleteness, fragmentation, and privacy, this work unlocks greater potential for advanced machine learning techniques to be practically applied, enhancing clinical decision-making, patient care, and overall healthcare outcomes.I would like to thank my family, including my wife, parents, sister, and everyone else for all of their love and support throughout this process.I would also like to thank Professor Jimeng Sun for all of his guidance in each of my research, my academics, and my career.Next, I want to express my gratitude to Professor Arindam Banerjee, Professor Jim Rehg, and Dr. Cao Xiao for agreeing to serve on my committee and providing invaluable feedback and advice.Additionally, I would like to show appreciation to Dr. Cao Xiao, Dr. Lucas Glass, and Dr. Faraz Faghri for serving as both collaborators and mentors throughout my growth as a researcher.Furthermore, I would like to recognize all of the members of my research lab
Author: Theodorou, Brandon
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7220806477