Mapping Common Data Elements to a Domain Model Using an Artificial Neural Network

Abstract

The medical community uses a variety of data standards for clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) is one such standard that provides robust data point definitions. The Biomedical Research Integrated Domain Group (BRIDG) model is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Manually mapping the CDEs to the BRIDG model can facilitate mapping the CDEs to other standards. Unfortunately, the error-prone, labor-intensive manual mapping process creates a significant barrier for researchers who use CDEs. In this paper, we present our preliminary work to develop a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1,284 CDEs mapped to BRIDG classes as the gold standard to train and obtain the appropriate weights of six CDE attributes. Finally, we recommended a list of candidate BRIDG classes to which the CDE of interest may belong. Preliminary testing has proven the effectiveness and efficiency of our proposed methodology in mapping CDEs to BRIDG classes.