Connecting the Dots
From Big Data to Healthy Heart
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Rising capacity to measure extensive arrays of biological parameters has ushered in an era of biomedical big data. As massive datasets from large cohorts become the norm, the discipline of data science has emerged to tackle data-driven problems at the intersection of biomedical research and patient care. We introduce several sources of cardiovascular big data and discuss the importance of maximizing participation in data-driven knowledge production models.
What Are Big Data?
Every day, our world produces a staggering 2.5 quintillion (1018) bytes of data, including a steadily increasing amount of data from health care and biomedicine. The whole-genome sequence of a patient can reach 100 gigabytes (1011 bytes) in size, whereas a cardiology division may perform >1000 echocardiograms per month, totaling >200 gigabytes of data. The term biomedical big data has been coined to describe healthcare and biomedical datasets that reach remarkable scale, volume, or complexity. Four biomedical big data sources are of particular interest to cardiovascular biomedicine:
Functional phenotypes: Demographics, hemodynamics, electrocardiography, echocardiograms, and imaging data are pouring in from large cohorts such as from among the ≈11 500 cardiac-related studies that are listed by clinicaltrials.gov. Popular personal fitness-tracking devices likewise have created a deluge of mobile health data (eg, heart rate, physical activities, lifestyle) awaiting exploitation. The ability to extract features from phenotypic data and to identify complex interrelationships offers tremendous potential to enhance diagnoses and to improve care.
Molecular profiles: Large-scale omics data on genes, transcripts, proteins, and metabolites can now be acquired in large …