A Standard Based Approach for Biomedical Knowledge Representation

The new generation of health information standards, where the syntax and semantics of the content is explicitly formalized, allows for interoperability in healthcare scenarios and analysis in clinical research settings. Genomic data as well may be saved in formats that enable computational analysis. Still, studies involve many artifacts that describe the accumulating knowledge as relationships between varied information items. These include genotype-phenotype associations and associations within the genomic and clinical worlds. Some involve analysis results targeted at a specific disease; others are of predictive nature specific to a patient and may be used by decision support applications. Representing knowledge is as important as representing data since data is more useful when coupled with relevant knowledge. Any further analysis and cross-research collaboration would benefit from persisting knowledge. This paper describes a methodology, used in Hypergenes (an EC FP7 project targeting Essential Hypertension), which captures data and knowledge using standards such as HL7 CDA and Clinical Genomics, aligned with the CEN EHR 13606 specification. We demonstrate the benefits of such an approach for clinical research as well as in healthcare oriented scenarios.