Leveraging Graph Models to Design Acute Kidney Injury Disease Research Data Warehouse

Abstract

The increased adoption of electronic medical records (EMR) systems and emergence of clinical data warehouses to integrate data from disparate data sources energized clinical research and prompted the biomedical informatics community to envision and implement efficient and effective tools to facilitate conduct of research. Data warehousing, traditionally built using relational database technology, provides a valuable platform to provide clinical data for secondary use. Though relational models proved solid in data management applications across industries, the complexity and variety of clinical data require an agile technical environment that can respond to evolving research data needs. A property graph model's data connectedness, data exploration, and visualization capabilities make it a strong candidate to store and manage clinical facts. As a proof of concept, the paper uses acute kidney injury (AKI) disease, an important and often overlooked disease process, to model AKI clinical data extracted from institutional data warehouse in a property graph model, represented by nodes (entities) and edges (relationships). The resulting graph model provides a powerful tool to visualize and explore AKI entities and relationships, and allows clinical researchers to answer clinically-relevant questions in an agile, data-driven, horizontally scalable environment.