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Population-Wide Analysis BundleData Science Bundle (draft version)


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This population-wide analysis bundle provides researchers with real-time access to data on large patient populations at multiple healthcare organizations. It includes i2b2, which enables query and analysis of data within an institution, and SHRINE (Shared Health Research Information Network), which is a federated query tool that connects different sites’ i2b2 systems. In this bundle, patient-level data never leaves an institution. The patient data are stored locally within each site’s i2b2 database, and only aggregate counts and statistics are shared with others in the network through SHRINE. The bundle also includes a common ontology called ACT (Accrual for Clinical Trials), which has been implemented in a SHRINE network with more than 50 institutions and 125 million patients. This data science bundle supports complex analyses of real-world clinical and genomic data. It includes i2b2, which enables query and cohort identification, and tranSMART, adds a suite of tools for data exploration, R-based advanced analytics (e.g., correlation analysis, heat maps, PCA, etc.), and genomic modules for Genome Wide Association Studies (GWAS) and high dimensional data analysis such as RNAseq.
Digital Twin Bundle

A Digital Twin is a concise, current, and true representation in silico of a functioning real-world entity. With origins in industry manufacturing and design, it can be used to assist the assembly of many complex and interacting parts prior to an analysis. In healthcare, the creation of a Digital Twin of a person consists of assembling data from many sources and calculating the assembled result to obtain an accurate representation of an individual. That representation can then be used with the assembles of other persons to run in silico studies. The Digital Twin makes possible more accurate population studies based upon real world data (RWD). Performing an extra layer of data reconciliation in the form of producing Digital Twins allows a population study based in Digital Twins to arrive at more accurate conclusions than when using raw data.


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Bundles and CDM BUN