TY - JOUR
T1 - Some experiences and opportunities for big data in translational research
AU - Chute, Christopher G.
AU - Ullman-Cullere, Mollie
AU - Wood, Grant M.
AU - Lin, Simon M.
AU - He, Min
AU - Pathak, Jyotishman
PY - 2013/10
Y1 - 2013/10
N2 - 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.
AB - 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.
KW - big data
KW - clinical data representation
KW - genomics
KW - health information technology standards
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U2 - 10.1038/gim.2013.121
DO - 10.1038/gim.2013.121
M3 - Review article
C2 - 24008998
AN - SCOPUS:84885094686
SN - 1098-3600
VL - 15
SP - 802
EP - 809
JO - Genetics in Medicine
JF - Genetics in Medicine
IS - 10
ER -