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Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

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  • Sheikh S. Abdullah

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

  • Neda Rostamzadeh

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

  • Kamran Sedig

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

  • Amit X. Garg

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

  • Eric McArthur

    (ICES, London, ON N6A 3K7, Canada)

Abstract

Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:2:p:33-:d:338530
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    References listed on IDEAS

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    1. Margaret Varga & Caroline Varga, 2016. "Visual Analytics: Data, Analytical and Reasoning Provenance," Springer Proceedings in Business and Economics, in: Victoria L. Lemieux (ed.), Building Trust in Information, pages 141-150, Springer.
    2. McCullagh, Peter, 1984. "Generalized linear models," European Journal of Operational Research, Elsevier, vol. 16(3), pages 285-292, June.
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    Cited by:

    1. 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.

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