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S39: BigData and Machine Learning for Integrative Health and Medicine

15:00 - 16:30, Tuttle, Terrace Level

David Lary (1), George Kaplan (2), Steven Woolf (3), Kirstin Aschbacher (4)

(1): University of Texas at Dallas, United States                                   

(2): University of Michigan, United States

(3): Virginia Commonwealth University, United States

(4): University of California San Francisco (UCSF), United States



BigData should be a key component of a holistic approach to integrative health and medicine. 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 Integrative Health professionals may not be familiar with these tools.