Abstract: We will provide a constructive definition of “Big Biomedical/Health Data” and provide examples of the challenges, algorithms, processes, and tools necessary to manage, aggregate, harmonize, process, and interpret such data. In data science, time complexity frequently manifests as sampling incongruency, heterogeneous scales, and intricate dependencies. We will present the concept of 2D complex-time (kime) and illustrate how the kime-order (time) and kime-direction (phase) affect advanced predictive analytics and scientific inference based on Big Biomedical Data. Kime-representation solves the unidirectional arrows of time problems, e.g., psychological arrow of time reflects the irrevocable past to future flow and thermodynamic arrow of time reflecting the relentless growth of entropy. Albeit kime-phase angles may not always be directly observable, we will illustrate how they can be estimated and used to improve the resulting space-kime modeling, trend forecasting, and predictive data analytics. Simulated data, clinical observations (e.g., neurodegenerative disorders), and multisource census-like datasets (e.g., UK Biobank) will be used to demonstrate time-complexity and inferential-uncertainty.

Bio: Ivo D. Dinov is a professor of Health Behavior and Biological Sciences and Computational Medicine and Bioinformatics at the University of Michigan. He directs the Statistics Online Computational Resource, the Integrative Biostatistics and Informatics Core of the Michigan Nutrition and Obesity Research Center, and the Udall Parkinson’s Disease Biostatistics and Data Management Core. He co-directs the Center for Complexity and Self-management of Chronic Disease (CSCD Center) and the multi-institutional Probability Distributome Project. Dr. Dinov is an Associate Director for Education and Training of the Michigan Institute for Data Science (MIDAS). He is a member of the American Statistical Association (ASA), the International Association for Statistical Education (IASE), the American Medical Informatics Association (AMIA), and the American Association for the Advancement of Science (AAAS), as well as an Elected Member of the Institutional Statistical Institute (ISI).