| dc.description.abstract | Electronic health records (EHRs) provide unprecedented amounts
of data, facilitating entirely new kinds of observational research
studies.
One valuable and challenging area of research enabled by these
databases is characterizing and exploring clinical phenotypes as
represented by billing codes, asking questions such as how
phenotypes are associated with biomarkers or each other.
Analyses involving EHR derived phenotypes have seen great
success but typically operate under assumptions of independence
between the phenotypes themselves, a considerable simplification
of the intricate relational structure that drives the human phenome.
By acknowledging and incorporating the phenome's network
structure into analyses, we can both enhance existing techniques
and develop new methods for exploring this high-dimensional
space.
In this dissertation, we illustrate this with three products. PhewasME is an application that augments Phenome-Wide Association
Studies by displaying typically provided tables and plots alongside
with the underlying patient-phenotype network. Next,
AssociationSubgraphs is a new algorithm and visualization for
examining high-dimensional association patterns, such as the
1,815-dimensional phenome encoded by PheCodes. Finally, we
demonstrate the use of network statistics to characterize topological
conservation between two comorbidity networks derived from
independent EHRs. | |
