Charles N. Mead

The BRIDG Project: A Technical Report

OBJECTIVES The Biomedical Research Integrated Domain Group (BRIDG) project is a collaborative initiative between the National Cancer Institute (NCI), the Clinical Data Interchange Standards Consortium (CDISC), the Regulated Clinical Research Information Management Technical Committee (RCRIM TC) of Health Level 7 (HL7), and the Food and Drug Administration (FDA) to develop a model of the shared understanding of the semantics of clinical research. DESIGN The BRIDG project is based on open-source collaborative principles and an implementation-independent, use-case driven approach to model development. In the BRIDG model, declarative and procedural knowledge are represented using the Unified Modeling Language (UML) class, activity and state diagrams. MEASUREMENTS The BRIDG model currently contains harmonized semantics from four project use cases: the caXchange project and the patient study calendar project from caBIG; the standard data tabular model (SDTM) from CDISC; and the regulated products submission model (RPS) from HL7. Scalable harmonization processes have been developed to expand the model with content from additional use cases. RESULTS The first official release of the BRIDG model was published in June 2007. Use of the BRIDG model by the NCI has supported the rapid development of semantic interoperability across applications within the caBIG program. CONCLUSIONS The BRIDG project has brought together different standards communities to clarify the semantics of clinical research across pharmaceutical, regulatory, and research organizations. Currently, the NCI uses the BRIDG model to support interoperable application development in the caBIG, and CDISC and HL7 are using the BRIDG model to support standards development.

A framework for evaluating and utilizing medical terminology mappings.

Use of medical terminologies and mappings across them are considered to be crucial pre-requisites for achieving interoperable eHealth applications. Built upon the outcomes of several research projects, we introduce a framework for evaluating and utilizing terminology mappings that offers a platform for i) performing various mappings strategies, ii) representing terminology mappings together with their provenance information, and iii) enabling terminology reasoning for inferring both new and erroneous mappings. We present the results of the introduced framework from SALUS project where we evaluated the quality of both existing and inferred terminology mappings among standard terminologies.