Challenges in Using a Graph Database to Represent and Analyze Mappings of Cancer Study Data Standards.

While using data standards can facilitate research by making it easier to share data, manually mapping to data standards creates an obstacle to their adoption. Semi-automated mapping strategies can reduce the manual mapping burden. Machine learning approaches, such as artificial neural networks, can predict mappings between clinical data standards but are limited by the need for training data. We developed a graph database that incorporates the Biomedical Research Integrated Domain Group (BRIDG) model, Common Data Elements (CDEs) from the National Cancer Institute's (NCI) cancer Data Standards Registry and Repository, and the NCI Thesaurus. We then used a shortest path algorithm to predict mappings from CDEs to classes in the BRIDG model. The resulting graph database provides a robust semantic framework for analysis and quality assurance testing. Using the graph database to predict CDE to BRIDG class mappings was limited by the subjective nature of mapping and data quality issues.