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Visual Analytics of Image-Centric Cohort Studies in Epidemiology

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Visualization in Medicine and Life Sciences III

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Epidemiology characterizes the influence of causes to disease and health conditions of defined populations. Cohort studies are population-based studies involving usually large numbers of randomly selected individuals and comprising numerous attributes, ranging from self-reported interview data to results from various medical examinations, e.g., blood and urine samples. Since recently, medical imaging has been used as an additional instrument to assess risk factors and potential prognostic information. In this chapter, we discuss such studies and how the evaluation may benefit from visual analytics. Cluster analysis to define groups, reliable image analysis of organs in medical imaging data and shape space exploration to characterize anatomical shapes are among the visual analytics tools that may enable epidemiologists to fully exploit the potential of their huge and complex data. To gain acceptance, visual analytics tools need to complement more classical epidemiologic tools, primarily hypothesis-driven statistical analysis.

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Notes

  1. 1.

    http://www.erasmus-epidemiology.nl/research/ergo.htm, accessed: 1/31/2016.

  2. 2.

    http://org.UiB.no/aldringsprosjektet/, accessed: 1/31/2016.

  3. 3.

    http://www.ukbiobank.ac.uk, accessed: 1/31/2016.

  4. 4.

    http://www.nationale-kohorte.de/, accessed: 1/31/2016.

  5. 5.

    http://www-01.ibm.com/software/analytics/spss/products/statistics/, accessed: 1/31/2016.

  6. 6.

    http://www.r-project.org/ accessed: 1/31/2016.

  7. 7.

    http://www.stata.com/, accessed: 1/31/2016.

  8. 8.

    http://www.tableausoftware.com/, accessed: 1/31/2016.

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Acknowledgements

We want to thank Lisa Fraunstein, David Kilias and David Perlich who supported our analysis of the SHIP data as student workers as well as Marko Rak who provided the detection algorithm for the vertabrae and Myra Spilopoulou for fruitful discussions on clustering and data mining (all University of Magdeburg). We thank Martijn Steenwijk for providing images from his work and Charl Botha for fruitful discussions. Matthias Günther (Fraunhofer MEVIS) explained us quality aspects of MR imaging in epidemiologic studies. This work was supported by the DFG Priority Program 1335: Scalable Visual Analytics. SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grant no. 03ZIK012), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania.

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Authors and Affiliations

  1. Otto-von-Guericke University Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Bernhard Preim, Paul Klemm, Steffen Oeltze & Klaus Toennies

  2. University of Bergen, Høyteknologisenteret (HiB), Thormøhlensgate 55, 5008, Bergen, Norway

    Helwig Hauser

  3. University of Greifswald, Institut für Community Medicine, Walter Rathenau Str. 48, 17475, Greifswald, Germany

    Katrin Hegenscheid & Henry Völzke

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  1. Bernhard Preim
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  2. Paul Klemm
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  4. Katrin Hegenscheid
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Correspondence to Paul Klemm .

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Editors and Affiliations

  1. Department of Computer Science and Elect, Jacobs University, Bremen, Germany

    Lars Linsen

  2. Department of Computer Science, University of California, Davis, California, USA

    Bernd Hamann

  3. Visualization and Data Analysis, Zuse Institute Berlin (ZIB), Berlin, Germany

    Hans-Christian Hege

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Preim, B. et al. (2016). Visual Analytics of Image-Centric Cohort Studies in Epidemiology. In: Linsen, L., Hamann, B., Hege, HC. (eds) Visualization in Medicine and Life Sciences III. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-24523-2_10

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