A retrospective cohort study is a medical research study in which the patient records of a group of similar individuals are compared for a particular outcome. For instance, a study can try to assess the impact of smoking behavior with respect to getting lung cancer in a group of 40-year old construction workers who also have been exposed to asbestos. As retrospective case studies are historical in nature, researchers require accurate representations of patient records over time in order to correctly assess the importance of particular time-dependent patient characteristics.
During this presentation, we will show how state-of-the-art Graph Databases can be extended with a set of temporal primitives that effectively aid researchers at gathering the required insights from a set of longitudinal medical records. Graph Databases are the ideal platform to model and store the multi-dimensional data points of the individual patient records and the cohorts to which they are belonging. By introducing a temporal notion within Graph Databases, physicians are given the power to query beyond time boundaries and get historical access to individual patient characteristics or combinations thereof. Patterns for individual patients can be compared and evaluated against the patterns for the cohort.
In order to validate our proposed approach, we have implemented FluxGraph, a proof-of-concept Temporal Graph Database. Being Blueprints-compatible, it should be straightforward to integrate the proposed API changes within mature Graph Database products. The explicit notion of time, combined with the flexible modelling offered by Graph Databases, provides users with an expressive and powerful data store and analysis platform which is difficult or even impossible to implement with traditional relational database technologies.
