title: Some experiences and opportunities for big data in translational research.
abstract: Health care has become increasingly information intensive. The advent of genomic data, integrated into patient care, significantly accelerates the complexity and amount of clinical data. Translational research in the present day increasingly embraces new biomedical discovery in this data-intensive world, thus entering the domain of "big data." The Electronic Medical Records and Genomics consortium has taught us many lessons, while simultaneously advances in commodity computing methods enable the academic community to affordably manage and process big data. Although great promise can emerge from the adoption of big data methods and philosophy, the heterogeneity and complexity of clinical data, in particular, pose additional challenges for big data inferencing and clinical application. However, the ultimate comparability and consistency of heterogeneous clinical information sources can be enhanced by existing and emerging data standards, which promise to bring order to clinical data chaos. Meaningful Use data standards in particular have already simplified the task of identifying clinical phenotyping patterns in electronic health records.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
authors.forename: Mollie
authors.initials: M
authors.lastname: Ullman-Cullere
authors.forename: Grant M
authors.initials: GM
authors.lastname: Wood
authors.forename: Simon M
authors.initials: SM
authors.lastname: Lin
authors.forename: Min
authors.initials: M
authors.lastname: He
authors.forename: Jyotishman
authors.initials: J
authors.lastname: Pathak
mesh_terms: D016208:Databases, Factual; D057286:Electronic Health Records; D005820:Genetic Testing; D005826:Genetics, Medical; D023281:Genomics; D006801:Humans; D016247:Information Storage and Retrieval; D062527:Meaningful Use; D008490:Medical Informatics / Q000592:standards*; D010641:Phenotype; D057170:Translational Research, Biomedical / Q000639:trends