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Graph-based visualization of sensitive medical data

  • 1191: Sentient Multimedia Systems
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Abstract

With the increasing amounts of electronic health data being constantly generated in medical examinations and by sensors and mobile applications, data visualization methods can assist medical professionals and researchers in exploring and making sense of the data. Two important challenges faced by data visualization are large data volume and protection of sensitive data. In this paper, we propose a graph-based method that allows the exploration of a patient dataset, while also naturally allowing the summarization of large amounts of data, making it applicable to large datasets and sensitive data. A graph is constructed from the raw data, encoding local similarities among patients, and is visualized on the screen, producing a visual map of the patient distribution. Multidimensional glyphs are put in place of the nodes, revealing the properties that characterize each graph area. The graph construction method is extended to an incremental scheme, allowing federated graph formation. The proposed method is demonstrated in three use cases, regarding frailty in older adults, Sjögren’s Syndrome patients, and a large-size diabetes dataset.

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Notes

  1. https://nodejs.org/

  2. https://d3js.org/

  3. https://www.kaggle.com/brandao/diabetes

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Acknowledgements

This work has been supported by the EU H2020 projects FrailSafe (H2020-PHC-21-2015, grant agreement no. 690140) and HarmonicSS (H2020-SC1-2016-RTD, grant agreement no. 731944).

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Authors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Harilaou - Thermi, 57001, Thermi - Thessaloniki, Greece

    Ilias Kalamaras, Konstantinos Glykos, Konstantinos Votis & Dimitrios Tzovaras

  2. Computer Engineering and Informatics Department, University of Patras, 26504, Patras, Greece

    Vasilis Megalooikonomou

Authors
  1. Ilias Kalamaras
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  2. Konstantinos Glykos
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  3. Vasilis Megalooikonomou
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  4. Konstantinos Votis
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  5. Dimitrios Tzovaras
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Correspondence to Ilias Kalamaras.

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Kalamaras, I., Glykos, K., Megalooikonomou, V. et al. Graph-based visualization of sensitive medical data. Multimed Tools Appl 81, 209–236 (2022). https://doi.org/10.1007/s11042-021-10990-1

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  • DOI: https://doi.org/10.1007/s11042-021-10990-1

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