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VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data

Author

Listed:
  • Maede S. Nouri

    (Insight Lab, Western University, London, ON N6A 3K7, Canada)

  • Daniel J. Lizotte

    (Department of Computer Science, Faculty of Science and Department of Epidemiology and Biostatistics, Western University, London, ON N6A 3K7, Canada)

  • Kamran Sedig

    (Insight Lab, Western University, London, ON N6A 3K7, Canada)

  • Sheikh S. Abdullah

    (Insight Lab, Western University, London, ON N6A 3K7, Canada)

Abstract

Multimorbidity is a growing healthcare problem, especially for aging populations. Traditional single disease-centric approaches are not suitable for multimorbidity, and a holistic framework is required for health research and for enhancing patient care. Patterns of multimorbidity within populations are complex and difficult to communicate with static visualization techniques such as tables and charts. We designed a visual analytics system called VISEMURE that facilitates making sense of data collected from patients with multimorbidity. With VISEMURE, users can interactively create different subsets of electronic medical record data to investigate multimorbidity within different subsets of patients with pre-existing chronic diseases. It also allows the creation of groups of patients based on age, gender, and socioeconomic status for investigation. VISEMURE can use a range of statistical and machine learning techniques and can integrate them seamlessly to compute prevalence and correlation estimates for selected diseases. It presents results using interactive visualizations to help healthcare researchers in making sense of multimorbidity. Using a case study, we demonstrate how VISEMURE can be used to explore the high-dimensional joint distribution of random variables that describes the multimorbidity present in a patient population.

Suggested Citation

  • Maede S. Nouri & Daniel J. Lizotte & Kamran Sedig & Sheikh S. Abdullah, 2021. "VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data," Data, MDPI, vol. 6(8), pages 1-19, August.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:8:p:85-:d:608464
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    References listed on IDEAS

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    1. Dean P. Foster & Robert A. Stine, 2008. "α‐investing: a procedure for sequential control of expected false discoveries," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 429-444, April.
    2. Sheikh S. Abdullah & Neda Rostamzadeh & Kamran Sedig & Amit X. Garg & Eric McArthur, 2020. "Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3," Data, MDPI, vol. 5(2), pages 1-24, March.
    3. Wullianallur Raghupathi & Viju Raghupathi, 2018. "An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health," IJERPH, MDPI, vol. 15(3), pages 1-24, March.
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