The main 2015-2016 accomplishments of the Methods and Analytics core include:

I. Data Dashboard

In this study (PMCID: PMC4520712), we developed a mechanism to integrate dispersed multi-source data and service the mashed information via human and machine interfaces in a secure, scalable manner. This process facilitates the exploration of subtle associations between variables, population strata, or clusters of data elements, which may be opaque to standard independent inspection of the individual sources. This new platform includes a device agnostic tool (Data Dashboard webapp​) for graphical querying, navigating and exploring the multivariate associations in complex heterogeneous datasets.

II. Data Visualizaiton

There is no fundamental theory for representation, analysis and inference. We developed a roadmap for uniform handling, visualization and analysis of such complex data remains elusive. This figure illustrates some of the graphical data visualization methods that we are employing for various classes of datasets. Technical details are available here (SMHS EBook) and here (SMHS Canvas).

III. Predictive Big Data Analytical Methods

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this study (PMCID: PMC4766610), we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols.