Big Data and Atrial Fibrillation: Current Understanding and New Opportunities
| Autor: | Qian-Chen Wang, Zhen-Yu Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Big Data Computer science Big data Pharmaceutical Science Disease 030204 cardiovascular system & hematology Machine Learning 03 medical and health sciences High morbidity 0302 clinical medicine Basic research Atrial Fibrillation Genetics medicine Animals Data Mining Humans Potential source Genetics (clinical) Mechanism (biology) business.industry Atrial fibrillation Precision medicine medicine.disease Data science Data Accuracy 030104 developmental biology Molecular Medicine Cardiology and Cardiovascular Medicine business |
| Zdroj: | Journal of cardiovascular translational research. 13(6) |
| ISSN: | 1937-5395 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink