Integrating Multi-Domain Electronic Health Data, Machine Learning, and Automated Cardiac Phenomics for Personalized Cardiovascular Care
Creators: name: Dykstra, Steven
Publisher: name: Cumming School of Medicine
Year: 2024
Subjects: subject: Bioinformatics
subject: FOS: Computer and information sciences
subject: Computer Science
subject: Artificial Intelligence

description: This thesis aims to address core challenges surrounding the integration of multi-domain cardiovascular data, inclusive of patient reported health, electronic health information, and diagnostic imaging, to support artificial intelligence (AI) based risk prediction modelling. Despite inaugural success surrounding the use of AI-driven approaches to leverage granular features from each respective data source, the lack of integration continues to limit a comprehensive representation of patient health critical to the implementation of AI-augmented clinical decision support (AI-CDS). Central to this thesis was the primary hypothesis that patient-consented migration, integration, and curation of disparate data sources can be achieved in real-world clinical environments, permitting longitudinal accumulation of standardized resources for machine learning-based risk modelling. To test this hypothesis, my first aim was to develop a software infrastructure to establish and maintain a precision health data model for cardiovascular care. This data model forms the foundation of the Cardiovascular Imaging Registry of Calgary (CIROC), a platform which to date has generated structured data resources for over 20,000 unique patients with cardiovascular disease. The success of this robust data model has led to the expansion of this infrastructure to support all clinics of the Libin Cardiovascular Institute. The design of this initiative, called the PULSE program, was established as an objective of Aim 1, delivering a structured manuscript describing methods and recommendations for implementing a scalable institutional personalized medicine program for the ethical, fair, and equitable introduction of AI-CDS. Subsequently, the second aim demonstrates the value of the established data model, highlighting how it can be used for the development and validation of machine-learning based prediction models for cardiovascular outcomes. Utilizing multi-domain features of the CIROC data model, I demonstrated superiority of machine learning-based approaches over traditional risk prediction methods to predict new-onset atrial fibrillation, a leading cause of stroke. This study highlighted the value of integrating patient-reported health, electronic health record, and cardiac diagnostic data to forecast future cardiovascular events with improved accuracy. Further, my third aim targeted an expansion of disease features from source diagnostic testing data to improve risk modelling. To achieve this, I developed deep learning-based models for the automated analysis (segmentation and fiducial labelling) of the left ventricle from cine cardiac MRI imaging, enabling the delivery of 3D shape phenomics. This work showcases the capacity for deep learning techniques to further enhance the developed data models for patient-specific risk modelling by supporting advanced analyses of unique disease characteristics including shape and deformation. This novel solution is now planned for external validation by a large, international clinical study assessing the incremental value of 3D shape phenomics to improve prediction accuracy across a broad range of diseases. Overall, this thesis presents a comprehensive exploration of technical development required for, and value generated by multi-domain data integration for AI-CDS in cardiovascular care. Incremental to demonstrating feasibility, the deliverables of this thesis serve as a foundation for growth of an emerging institutional precision medicine initiative and for the development of future advanced multi-domain machine learning models relevant to cardiovascular care.