Abstract—Disease-specific Cohort data across different healthcare and clinical research entities is of paramount importance for the study of the particular disorder and the development of new clinical and health policies. However, the significant structural and semantic mismatches across these data stemming from their independent development prevent their computer-based processing. The formal expression of the individual Cohorts using a common formalism (Data Harmonization) is the means for producing valid and accurate results, especially for diseases affecting a small percentage of the population, such as is the primary Sjögren's Syndrome (pSS), in which case data analytics on an individual cohort may lead to results not easily generalizable and of low accuracy and trust-worthiness. In this work, the approach followed in the HarmonicSS project for bridging the gap among eight heterogeneous Cohorts from eight different Cohort providers is presented, which was based on the software-aided analysis of their individual data structure and terminology. One of the outcomes of this process was a number of reusable parameterizable correspondence patterns (named Mapping Scenarios) that were accordingly instantiated for the accurate and complete mapping of the Cohort data to the Reference Model elements. The mapping scenarios were incorporated in a Visual Mapping Tool, which was developed for facilitating their use from both ICT experts and non-expert users.
 

Index Terms—mapping scenarios, correspondence patterns, cohort study, data harmonization, semantic web

Cite: Efthymios Chondrogiannis, Efstathios Karanastasis, Vassiliki Andronikou, and Theodora Varvarigou, "Bridging the Gap among Cohort Data Using Mapping Scenarios," Journal of Advances in Information Technology, Vol. 12, No. 3, pp. 179-188, August 2021. doi: 10.12720/jait.12.3.179-188

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