Global Congress on Big DataExpect Big Data and Big Things at Your AHA Scientific Events
View the Global Congress track in the Online Program Planner for a detailed look at these Must-See Sessions.
Four Areas of Programming:
- Outcomes Research
- Mobile Health (mHealth)
- Basic Science/Genomics
- Electronic Medical Record (EMR) and a Learning Health Care System
- Engage in conversation on the worldwide importance of data collection and analysis
- Discuss Challenges of Big Data Science:
- Data Capture
- Understand Big Data Science in:
- Disease Mechanisms
- Accelerated Improvement in CVD care and prevention
- Epidemiological Determinants
Don’t Miss these presentations:
Harnessing the Power of Big Data-- Bench-to-Bedside, to Population
Sunday, November 16 at 8:00 am
- Gary H. Gibbons, M.D.
- Harlan Krumholz, M.D.
- Robert Califf, M.D.
- Joseph Loscalzo, M.D.
- John Gaziano, M.D.
Ethics and Governance of Big Data Collections for Research and Clinical Care
Monday, November 17 at 10:45 am
- Anne Wojcicki, Private Sector Perspective
- Angela Radcliffe, Patient Perspective on Patient Confidentiality and Data Sharing
- Harlan Krumholz, M.D., Academic Clinical Science Perspective
Program Chairs: Mikhail Kosiborod, MD, FAHA | Christopher J. O’Donnell, MD, MPH
- Michael Blum, MD
- Lora Burke PhD, MPH, RN, FAAN
- Lesley Curtis, PhD
- Richard Flanigan, SVP of Cerner Corporation
- Gregory Marcus, MD, MAS, FACC, FAHA, FHRS
- Christopher Newton-Cheh, MD, MPH, FAHA
- Anthony Rosenzweig, MD
- Joseph Ross, MD, MHS
- Nilesh J Samani, MD
- Erica Spatz, MD, MHS
This exciting four-day series of seminars and how-to sessions, occurring within Scientific Sessions 2014, will bring together leading experts from around the world on the application of big data science in basic, translational, genomic, clinical, population, quality and outcomes science. Participants and attendees will hear state-of-the-art information on big data science for understanding disease mechanisms, epidemiological determinants, and practice for the accelerated improvement in the prevention, treatment and clinical care of cardiovascular disease and stroke on a global scale. The importance of the collection, analysis, and leveraging of large scale data resources across the world will be addressed.
Big data refers to large and complex data sets that require tools and methods that go beyond traditional data processing applications. Big data science challenges include data capture, annotation, storage, sharing, transfer, analysis and visualization. Cardiovascular disease investigators, clinicians, information officers, and policy-makers are increasingly encountering large and complex datasets that surpass limitations in areas including genomics and other 'omics, molecular imaging, human cardiovascular imaging, cardiovascular disease and stroke surveillance, critical care clinical decision-making, electronic health monitoring, and health system policy. These data sets are growing in size in part due to increased availability of multiplex digital data sources. We have entered an era of unprecedented opportunity to harness these evolving big data sources across the spectrum of basic, translational, clinical and population science.