Big Data Integration Case Study for Radiology Data Sources

Priya Deshpande1, Alexander Rasin1, Eli Brown1, Jacob Furst1, Daniela S. Raicu1, Steven M. Montner2, Samuel G. Armato III2

  • 1DePaul University
  • 2University of Chicago



Poster Session


10:00 - 17:00 | Tue 30 Oct | Foyer | B1P-D

Engineering for Life Sciences

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Today's digitized world urgently needs tools for Big Data integration and analysis. Healthcare domain generates petabytes of heterogeneous data each day; integration of these data can greatly improve the overall quality of patient care. To design a framework for medical data integration, we first developed IRIS search engine for radiologists. In this paper, we describe a case study of data integration for radiology data sources. Through this study, we learned that data integration is an iterative process that requires continuous integration of additional data sources. Our results show that each subsequent step of data integration further improved IRIS engine results.

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