The tool allows clinicians and researchers to develop cohorts across four key data areas: Clinical & Socioeconomic, Genomic, CT Imaging, and X-ray Imaging. Results of user queries are visualized in QlikView, to aid the identification of patterns of interest. Users can effortlessly traverse between and create patient cohorts using data from each of the data sets, enabling them to ask and answer questions at the speed of thought.
Users may then compare cohorts using various analyses completed in R analytics and visualized in QlikView, with different views tailored around the expertise of various stakeholder groups—genomic visualizations for geneticists, radiologic visualizations for radiologists, and clinical visualizations for clinicians. The QlikView visualizations enable researchers and clinicians to derive valuable insights that can support the direction of new clinical trials or manuscript development.
DEPOT is a model for the consolidation and analysis of interdisciplinary data that could be replicated and applied to other disease areas. DEPOT thus represents a significant step forward in the areas of healthcare analytics and collaborative research platforms.
For more information, please contact Darren Schneider at email@example.com.
Image 1: DEPOT enables users to begin their analysis with a specific data set of interest.
Image 2: QlikView integrates seamlessly with the overall .NET application.
Image 3: QlikView displays the result of R analytics, enabling users to visualize the differences between patient cohorts.
http://www.who.int/mediacentre/factsheets/fs104/en/, accessed 2/14/17