These 10 grand challenges may have major breakthroughs, research, and/or socioeconomic impacts in the next 5 to 10 years.

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

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Arrhythmogenic cardiomyopathy is distinguished from DCM by a propensity towards arrhythmia exceeding the degree of ventricular dysfunction. The left-dominant subtype is under-recognized owing to misattribution to other disorders and lack of specific diagnostic criteria. Clinicians are alerted to the possibility of LDAC in patients of any age with unexplained arrhythmia of LV origin, (infero)lateral T-wave inversion, apparent DCM (with arrhythmic presentation), or myocarditis (chest pain and enzyme rise with unobstructed coronary arteries).