BigData and Machine Learning for Public Health

BigData should be a key component of a holistic approach to public health. There

is increasing awareness that health is shaped by more than health care, but the exact

causal pathway that links health to health behaviors, socioeconomic conditions, and

environmental conditions has been inadequately explored by conventional epidemiologic

methods. Existing knowledge and conventional research tools are often insufficient

to predict a priori how various environmental, social, psychological, behavioral,

and biological factors are interrelated and change over time. Human health is an

interdependent multifaceted system. The quantity of data that is now available through

new technologies requires different analytic methods and approaches. An exciting

new era is dawning where we are using these valuable data together (fully multi-
variately) with computational techniques such as machine learning to provide insights

for integrative health in the areas of methodology for patient care, scientific discovery,

decision support, and policy formulation. This session will showcase new advances for

those who would like to leverage the computational BigData revolution for integrative

health insight generation and describe some areas of exciting future development. Upon

completion attendees will have an appreciation of the tremendous value of using the

methodology of BigData and Machine Learning for Integrative Health. This is timely as

many Public Health professionals may not be familiar with these tools.