Advances in computing capabilities are palpably evident throughout many industries manifest by unprecedented, large-scale data integration and inferencing. Branded as "big-data" in many cases, the question of whether such techniques can leverage advances in biomedicine and clinical practice are obvious. High-throughput clinical analytics, synthesizing genomic and clinical attributes of a particular patient, portends predictive models that can directly influence clinical care decisions. However, to make this widely shared vision practical and scalable, barriers attributable to data heterogeneity dominate. Methods and strategies to increase the comparability and consistency of healthcare related data will be discussed.
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