Cardiovascular disease (CVD) affects millions of people and is a leading cause of death worldwide. CVD consists of a broad set of conditions including structural heart disease, coronary artery disease and stroke. Risk for each of these conditions accumulates over long periods of time depending on several risk factors. In order to reduce morbidity and mortality due to CVD, preventative treatments administered prior to first CVD event are critical. According to clinical guidelines, such treatments should be guided by an individual’s total risk within a window of time. A related objective is secondary prevention, or early detection, wherein the aim is to identify and mitigate the impact of a disease that has already taken effect. With the widespread adoption of electronic health records (EHRs), there is tremendous opportunity to build better methods for risk assessment and early detection.
However, existing methods which use EHRs are limited in several ways: (1) they do not leverage the full longitudinal history of patients, (2) they use a limited feature set or specific data modalities, and (3) they are rarely validated in broader populations and across different institutions. In this dissertation, I address each of these limitations. In Aim 1, I explore the challenge of handling longitudinal, irregularly sampled clinical data, proposing discriminative and generative approaches to model this data. In Aim 2, I develop a multimodal approach for the early detection of structural heart disease.
Finally, in Aim 3, I study how different feature inclusion choices affect the transportability of deep risk assessment models of coronary artery disease across institutions. Collectively, this dissertation contributes important insights towards building better approaches for risk assessment and early detection of CVD using EHR data and systematically assessing their transportability across institutions and populations.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/jkgw-nn30 |
| Date | January 2024 |
| Creators | Bhave, Shreyas Abhay |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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