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Big Data Analytics & Methods

Big data, with its volume, velocity, variety and veracity (https://www.ibmbigdatahub.com/infographic/four-vs-big-data), provide critical information that can be used to understand health patterns and healthcare utilization, cost, patient outcomes, and consumer behaviors. The potential of big data can be leveraged to drive evidence-based decision making and to turn disease data into meaningful insights for guiding patient care and health policy. We use a variety of big data methods to analyze data of different types and from different sources for public health research. Examples are using natural language processing on electronic medical records (EMR) to identify surgical site infections, analyzing consumer reports on social media (e.g. Facebook) to evaluate satisfaction with hospital and nursing home care, and the use of the super learner approach to predict risk for suicide death among older adults receiving long-term care. In addition to the EMR and social media data, the Department can support faculty and student research with more than 15 years of hospital and nursing home claims data as well as large and diverse longitudinal population datasets including those within the Multi-Ethnic Study of Atherosclerosis and the Framingham Heart Study.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4385465/

Mooney, S. J., Westreich, D. J., & El-Sayed, A. M. (2015). Epidemiology in the Era of Big Data. Epidemiology (Cambridge, Mass.)26(3), 390–394. http://doi.org/10.1097/EDE.0000000000000274

Program Faculty

Yue Li, PhD

 

Shubing Cai, Ph.D.

 

Helena Temkin-Greener, Ph.D., M.S.

 

Robert Block, M.D., M.P.H.

 

Peter J. Veazie, Ph.D.