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      Big Data and Atrial Fibrillation: Current Understanding and New Opportunities.

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          Abstract

          Atrial fibrillation (AF) is the most common arrhythmia with diverse etiology that remarkably relates to high morbidity and mortality. With the advancements in intensive clinical and basic research, the understanding of electrophysiological and pathophysiological mechanism, as well as treatment of AF have made huge progress. However, many unresolved issues remain, including the core mechanisms and key intervention targets. Big data approach has produced new insights into the improvement of the situation. A large amount of data have been accumulated in the field of AF research, thus using the big data to achieve prevention and precise treatment of AF may be the direction of future development. In this review, we will discuss the current understanding of big data and explore the potential applications of big data in AF research, including predictive models of disease processes, disease heterogeneity, drug safety and development, precision medicine, and the potential source for big data acquisition. Grapical abstract.

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          Author and article information

          Journal
          J Cardiovasc Transl Res
          Journal of cardiovascular translational research
          Springer Science and Business Media LLC
          1937-5395
          1937-5387
          December 2020
          : 13
          : 6
          Affiliations
          [1 ] Department of Cardiovascular Medicine, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, China.
          [2 ] National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha, China.
          [3 ] Department of Cardiovascular Medicine, the Second Xiangya Hospital, Central South University, No.139 Renmin Road, Changsha, Hunan, China. isaac_w@csu.edu.cn.
          Article
          10.1007/s12265-020-10008-5
          10.1007/s12265-020-10008-5
          32378163
          fe04eb18-9638-46f2-a050-144c3203e760
          History

          Precision medicine,Atrial fibrillation,Big data,Machine learning

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